# A Survey of Evaluation Metrics Used for NLG Systems

## 1 Introduction to NLG Evaluation Metrics

### 1.1 Importance and Role of Evaluation Metrics in NLG

The critical role of evaluation metrics in the realm of Natural Language Generation (NLG) cannot be overstated; they serve as the cornerstone for gauging the effectiveness and quality of NLG systems. Evaluation metrics act as navigational aids, guiding the development and refinement of NLG models towards enhanced performance and utility. They provide essential feedback loops, enabling researchers and developers to iteratively improve their models, ultimately contributing to the advancement of the field.

These metrics serve dual purposes: first, they offer quantitative insights into the performance of NLG systems, allowing for objective comparisons among different models. This quantification is indispensable for model selection, as it facilitates the identification of superior performing systems in terms of specific criteria such as fluency, coherence, informativeness, and relevance. Second, metrics guide the iterative process of model development by pinpointing strengths and weaknesses, thus informing necessary adjustments and enhancements. Without robust evaluation metrics, the process of refining NLG models would be akin to navigating a ship without a compass, rendering progress unpredictable and inefficient.

One of the primary functions of evaluation metrics is to measure the quality of generated texts relative to ideal outputs. These metrics often gauge textual similarity, structural correctness, and semantic appropriateness. Traditional metrics like BLEU and ROUGE have long served this purpose, comparing NLG outputs with reference texts to estimate their fidelity and accuracy. While these metrics have been instrumental in driving early advancements in NLG, they are increasingly recognized as insufficient for capturing the full spectrum of quality attributes in modern NLG systems [1]. The emergence of large language models (LLMs) [2] has necessitated the evolution of evaluation metrics, as these models generate text that is less constrained by reference-based norms and more influenced by contextual understanding and creativity.

The advent of LLMs has underscored the limitations of traditional metrics, prompting the exploration of novel evaluation paradigms. Modern metrics now aim to assess the intrinsic quality of NLG outputs beyond mere surface-level features, incorporating aspects such as semantic richness, contextual appropriateness, and creative originality. For instance, metrics like BERTScore and MARS have been developed to better align with human judgments by leveraging contextual embeddings to capture deeper semantic nuances [3].

Moreover, evaluation metrics facilitate the systematic benchmarking of NLG systems across diverse tasks and domains. This benchmarking is crucial for the scientific validation of advancements in NLG technology, ensuring that claimed improvements are substantiated through rigorous testing. The GEM benchmark [4], for example, aims to provide a standardized platform for evaluating NLG systems across a variety of tasks, thereby promoting transparency and comparability in research outcomes.

Another significant role of evaluation metrics is in driving innovation by highlighting unexplored territories and unresolved challenges. The limitations of existing metrics often reveal gaps in current NLG capabilities, prompting targeted research efforts aimed at overcoming these limitations. For instance, the observation that no single metric can effectively correlate with human scores across various dimensions [5] has spurred investigations into multi-aspect evaluation frameworks that can provide a more comprehensive assessment of NLG performance.

Furthermore, evaluation metrics play a pivotal role in fostering interdisciplinary collaboration within the NLG community. By providing a common language and framework for discussing model performance, these metrics enable researchers and practitioners from diverse backgrounds to engage in meaningful dialogues and joint problem-solving initiatives. This collaborative spirit is vital for the holistic development of NLG technology, as it encourages the exchange of ideas and expertise that might otherwise remain isolated within specialized subfields.

In conclusion, the importance of evaluation metrics in NLG lies in their ability to provide objective, measurable, and actionable insights into the quality and effectiveness of NLG systems. These metrics are indispensable tools for steering model development, facilitating scientific benchmarking, and catalyzing innovation. As the field continues to evolve, so too must the evaluation metrics, adapting to new challenges and opportunities presented by advances in NLG technology. The continuous improvement and diversification of evaluation metrics are therefore essential for sustaining the momentum of NLG research and development, ultimately leading to more sophisticated, adaptable, and impactful NLG systems.

### 1.2 Types of NLG Systems and Tasks

Natural Language Generation (NLG) encompasses a wide array of tasks and systems, each with unique requirements and challenges. The diversity and complexity of NLG applications underscore the necessity of a robust evaluation framework capable of addressing the nuances of each task. This section provides an overview of various types of NLG systems and tasks, including text summarization, image captioning, dialogue generation, and question generation, to illustrate the breadth and depth of NLG applications.

**Text Summarization**

Text summarization involves the automatic generation of concise summaries from longer documents. This task can be categorized into extractive and abstractive summarization, depending on whether the summary is composed of segments from the original text or created anew using natural language processing techniques. Extractive summarization aims to identify and select the most important sentences from the document, whereas abstractive summarization seeks to rephrase and condense the key points, often producing summaries that are more fluent and coherent. The goal is to retain the essence of the source text while making the information more accessible and digestible for readers.

One of the significant challenges in text summarization is ensuring that the generated summary is faithful to the original document and captures the intended meaning accurately. However, as highlighted in [6], deep learning-based summarization models can sometimes produce summaries that contain facts not present in the source text, a phenomenon known as hallucination. This issue underscores the need for evaluation metrics that can detect and mitigate such inaccuracies, thereby improving the reliability of the generated summaries.

**Image Captioning**

Another prominent application of NLG is image captioning, where the goal is to generate descriptive captions for images. This task requires the model to understand the visual content of the image and convert it into meaningful and coherent natural language descriptions. Image captioning models must possess both visual understanding capabilities and natural language generation abilities, making it a highly interdisciplinary task.

The evaluation of image captioning systems typically involves assessing the quality, relevance, and fluency of the generated captions. One of the notable advancements in this area is the development of evaluation metrics that leverage large language models (LLMs). These metrics can offer more comprehensive assessments by evaluating the semantic and pragmatic aspects of the generated captions, thereby providing a more holistic view of the model's performance.

**Dialogue Generation**

Dialogue generation, encompassing both task-oriented and open-domain dialogue systems, represents another critical application of NLG. Task-oriented dialogue systems are designed to assist users in completing specific tasks, such as booking flights or ordering food, while open-domain dialogue systems aim to engage users in more general conversations. The success of these systems depends on their ability to generate responses that are contextually appropriate, informative, and engaging.

In the realm of task-oriented dialogue, NLG systems must be capable of generating responses that are relevant to the user’s intent and context. For instance, in the domain of open-domain question answering, NLG models must generate answers that are not only factually accurate but also responsive to the nuances of the question and the broader context of the conversation. Additionally, these models must handle a vast ontology of possible slot types and integrate information from previous turns in the dialogue, adding to the complexity of the task.

The evaluation of dialogue generation systems often involves assessing the coherence, informativeness, and engagement of the generated responses. Traditional metrics like BLEU and ROUGE, while useful for certain aspects of NLG evaluation, may fall short in capturing the multifaceted nature of dialogue generation. Consequently, there is a growing interest in developing more sophisticated evaluation metrics that can provide a more nuanced assessment of dialogue systems.

**Question Generation**

Question generation involves the automatic creation of questions based on given texts or passages. This task is particularly relevant in educational and testing contexts, where it can aid in the assessment of reading comprehension and learning outcomes. Unlike other NLG tasks, question generation places a strong emphasis on the answerability of the generated questions, ensuring that the questions can be accurately answered based on the provided text.

PMAN is a novel automatic evaluation metric designed specifically for question generation tasks. PMAN evaluates the answerability of generated questions by analyzing their relevance to the source text and their potential to elicit accurate responses. This metric complements traditional reference-based metrics like BLEU and ROUGE, which may not adequately capture the answerability aspect of generated questions.

**Synthetic Traffic Generation (STG)**

Synthetic traffic generation (STG) refers to the generation of synthetic data for the purpose of testing and validating natural language understanding and generation systems. STG is particularly relevant in the development and evaluation of conversational agents and QA systems, where the availability of high-quality training data is often limited.

The evaluation of STG tasks requires metrics that can assess the linguistic variability and representativeness of the generated texts. These metrics must ensure that the synthetic data reflects the diversity of natural language usage and is representative of real-world scenarios. The development of task-specific metrics for STG underscores the importance of tailoring evaluation methodologies to the specific requirements of different NLG tasks.

**Decomposed Question Answering**

Decomposed question answering (DQA) involves breaking down complex questions into simpler sub-questions and generating answers accordingly. This approach is particularly useful in domains where the complexity of the question exceeds the capacity of a single response. DQA models must be able to parse complex questions, identify relevant sub-questions, and generate coherent and informative responses.

Evaluating DQA systems presents unique challenges, as the generated responses must be evaluated based on their ability to accurately answer the decomposed sub-questions while maintaining coherence and relevance to the overall question. Metrics like DecompEval leverage instruction-tuned pre-trained language models to evaluate generated texts in an unsupervised manner, focusing on their ability to capture the nuances of decomposed questions.

**Conclusion**

The diverse landscape of NLG applications highlights the need for comprehensive and context-aware evaluation metrics that can accurately assess the performance of NLG systems across different tasks and domains. Each type of NLG task, from text summarization and image captioning to dialogue generation and question generation, presents unique challenges and requirements that must be addressed in the evaluation process. By recognizing the distinct characteristics of each NLG task, researchers and practitioners can develop more effective and reliable evaluation methodologies that promote the continued advancement of NLG technology.

### 1.3 Necessity of Effective Evaluation Metrics

The necessity of effective and comprehensive evaluation metrics in the realm of Natural Language Generation (NLG) systems cannot be overstated. Traditional metrics, such as BLEU and ROUGE, have long served as the cornerstone of NLG evaluations, yet they are increasingly recognized as inadequate for capturing the nuanced complexities inherent in NLG tasks. As NLG systems evolve in sophistication and application scope, the limitations of these traditional metrics become more apparent, necessitating a reevaluation of the current standards for NLG performance assessment.

One of the primary shortcomings of traditional metrics is their reliance on surface-level features such as n-gram overlap or longest common subsequences. While these metrics offer a quantitative measure of similarity between generated texts and reference texts, they often overlook deeper semantic and contextual nuances critical to NLG tasks. For instance, the BLEU metric, which measures the precision of n-grams in generated text compared to a reference, is known to favor grammatical correctness over semantic relevance [1]. This limitation is particularly evident in creative or open-ended NLG tasks where the generated text should exhibit a high degree of variability and originality—qualities that traditional metrics struggle to accurately assess.

Moreover, the inherent variability and subjectivity in human-generated reference texts pose significant challenges for traditional metrics. Different human evaluators might produce varying reference texts, leading to inconsistencies in evaluation outcomes. This variability underscores the need for metrics that are less reliant on high-quality reference texts and more capable of providing consistent evaluations regardless of the reference used. The emergence of reference-free metrics, such as those based on large language models (LLMs) [7], begins to address this issue by offering an evaluation method that does not depend on human-generated references.

Another critical aspect of effective evaluation metrics is their ability to provide a comprehensive assessment of NLG system performance across multiple dimensions. Traditional metrics often focus narrowly on a single aspect, such as fluency or relevance, while overlooking others equally important to NLG quality, such as coherence, informativeness, and engagement. The perturbation checklist approach introduced in "Perturbation CheckLists for Evaluating NLG Evaluation Metrics" highlights the inadequacy of relying solely on single-dimension metrics. By designing templates that target specific criteria and evaluating the system’s response to perturbations, this approach reveals that no single metric can effectively correlate with human judgments across all desired criteria for most NLG tasks. This underscores the need for multi-faceted metrics that can holistically assess NLG performance.

Furthermore, the rapid advancement in NLG technologies, driven by the emergence of large language models, necessitates evaluation metrics that can keep pace with these developments. LLMs have enabled significant improvements in NLG, facilitating the generation of more complex and contextually rich texts. However, these advancements also introduce new challenges for evaluation, as traditional metrics may not be robust enough to accurately reflect the performance of these sophisticated models. Metrics like DecompEval [8] offer promising alternatives by leveraging the power of instruction-tuned pre-trained language models to provide more nuanced and interpretable evaluations.

The evolving nature of NLG tasks also demands metrics that can adapt to the changing requirements of these tasks. As NLG applications expand into diverse domains, from healthcare to legal documentation, the need for task-specific evaluation metrics becomes increasingly evident. Metrics tailored to specific tasks, such as PMAN for question generation or metrics designed for synthetic traffic generation, highlight the importance of metrics finely tuned to the unique challenges and objectives of each NLG application. Such metrics can provide more accurate and meaningful evaluations by capturing the specific qualities most relevant to the given task.

Finally, the growing emphasis on ethical considerations in AI applications places additional pressure on the development of effective evaluation metrics. Ensuring that NLG systems generate text that is not only accurate but also ethical, unbiased, and culturally sensitive is essential. Metrics that can detect and penalize undesirable outputs, such as those generated by spurious correlations [9], are crucial in promoting responsible and ethical NLG practices. For example, metrics that are robust against adversarial attacks and can effectively evaluate the faithfulness and creativity of generated text are needed to ensure that NLG systems adhere to ethical standards.

In conclusion, the necessity of effective and comprehensive evaluation metrics in NLG systems is driven by the limitations of traditional metrics and the evolving nature of NLG tasks and technologies. As NLG continues to advance, the development of metrics that can accurately reflect the performance of these systems across various dimensions and tasks will be essential. These metrics must be robust, adaptable, and ethically sound, capable of providing consistent and meaningful evaluations regardless of the context in which NLG systems are deployed. By addressing the limitations of current metrics and embracing innovative approaches, the field of NLG can continue to progress towards more accurate, reliable, and ethically responsible systems.

## 2 Challenges and Difficulties in Automatic Evaluation

### 2.1 Semantic Understanding and Complexity

The evaluation of semantic complexity in Natural Language Generation (NLG) outputs remains a formidable challenge due to the intricate nature of semantic understanding, which encompasses not only the literal meaning of words but also their contextual nuances, pragmatics, and the implicit knowledge embedded in language use. This challenge is further compounded by the difficulty in automating the identification of semantically or pragmatically complex factual errors, as highlighted by the findings of the "Generation Challenges Results of the Accuracy Evaluation Shared Task."

To fully comprehend this issue, it is essential to delve into the multifaceted dimensions of semantic complexity. Semantics, the branch of linguistics concerned with meaning, involves the relationship between symbols and their meanings. In the context of NLG, this entails ensuring that generated text not only conveys accurate information but also does so in a manner that aligns with the intended meaning and context. Pragmatics, another critical aspect, addresses the use of language in social contexts and the implicit meanings conveyed beyond literal interpretations. Both semantic and pragmatic complexities demand sophisticated understanding mechanisms that current automatic evaluation metrics often fail to achieve effectively.

One of the primary limitations identified by the "Generation Challenges Results of the Accuracy Evaluation Shared Task" is the inability of existing metrics to reliably detect factual errors that are semantically or pragmatically complex. These errors can take various forms, such as misrepresentations of causality, temporal inconsistencies, or subtle shifts in tone that alter the perceived meaning of the text. Detecting such complexities requires an evaluation framework capable of distinguishing between literal correctness and meaningful accuracy. Traditional metrics like BLEU and ROUGE, though effective in certain scenarios, primarily focus on surface-level features such as n-gram overlaps and do not adequately capture the depth and breadth of semantic and pragmatic nuances.

Additionally, the reliance on lexical overlap as a primary indicator of quality is problematic when dealing with tasks that require the generation of text that captures the essence of input data without being bound to literal representations. For example, in summarization or paraphrasing tasks, conveying the core message while maintaining the integrity of the original meaning is crucial. Existing metrics often fall short in capturing these subtleties, leading to situations where generated text might receive high scores despite significant semantic deviations from the intended output.

The advent of large language models (LLMs) presents a promising avenue for addressing some of these challenges. LLMs, with their vast training datasets and deep learning architectures, possess a more nuanced understanding of language compared to earlier models. However, the evaluation of semantic complexity using LLMs is not without its own set of difficulties. While LLMs can provide more sophisticated assessments by leveraging their extensive knowledge base, they still face limitations in consistently detecting and correcting complex semantic errors. This is partly due to the fact that the internal mechanisms of LLMs, such as attention mechanisms and transformer layers, do not guarantee perfect semantic understanding. Moreover, the reliance on statistical patterns learned from vast amounts of text data means that the model’s performance is heavily influenced by the quality and diversity of its training data.

The "Unveiling LLM Evaluation Focused on Metrics Challenges and Solutions" paper underscores the potential for spurious correlations in reference-free evaluation metrics, which can further complicate the task of assessing semantic complexity. Spurious correlations, where metrics align with superficial features rather than deeper semantic qualities, can lead to misleading evaluations. For example, metrics might correlate highly with the length of the generated text or the use of rare words, which do not necessarily reflect the semantic accuracy or appropriateness of the generated content. This highlights the need for more robust evaluation strategies that can effectively differentiate between superficial and substantive semantic qualities.

Another significant challenge in evaluating semantic complexity arises from the varying degrees of difficulty across different NLG tasks. Tasks involving the generation of text from structured data, such as tables or databases, typically require a high level of semantic accuracy, given the precise relationships between data elements. In contrast, tasks like creative writing or story generation may place less emphasis on factual accuracy and more on narrative coherence and emotional resonance. Developing metrics that can adapt to these varying demands and accurately assess semantic complexity across diverse tasks remains a pressing research need.

Moreover, the subjective nature of semantic understanding adds another layer of complexity to the evaluation process. What might be considered semantically accurate in one context could be viewed as erroneous in another, depending on factors such as cultural background, personal experience, and situational context. This subjectivity poses a significant challenge for automatic evaluation metrics, which are inherently objective in their design. Incorporating human judgments into the evaluation process can help address this issue, but scaling such assessments to accommodate large datasets remains a practical hurdle.

Given these challenges, the development of more sophisticated and context-aware evaluation metrics is imperative. Metrics that can capture the intricate interplay between semantics and pragmatics, while also accounting for the contextual nuances of NLG outputs, would significantly enhance the reliability and validity of evaluations. Such metrics should be able to discern between literal correctness and meaningful accuracy, recognizing the importance of conveying the intended message effectively rather than merely matching surface-level features.

The "MetricEval, a Framework for Analyzing NLG Evaluation Metrics using Measurement Theory" introduces a novel framework that seeks to address these issues by providing a structured approach to conceptualizing and evaluating the reliability and validity of NLG evaluation metrics. By formalizing the sources of measurement error and offering statistical tools for empirical evaluation, MetricEval aims to enhance the interpretability and reliability of evaluation outcomes. This framework could serve as a valuable tool for researchers working on developing and refining metrics that are better suited to the evaluation of semantic complexity.

In conclusion, the evaluation of semantic complexity in NLG outputs is fraught with challenges, primarily due to the intricate nature of semantic understanding and the inherent limitations of existing metrics. While the emergence of LLMs and novel evaluation frameworks offer promising avenues for improvement, there remains a need for continued innovation and refinement in this domain. Addressing these challenges requires a concerted effort to develop metrics that can accurately capture the nuances of semantic and pragmatic complexity, ensuring that NLG systems generate text that is not only syntactically correct but also semantically meaningful and contextually appropriate.

### 2.2 Multifaceted Criteria Assessment

The evaluation of Natural Language Generation (NLG) systems often necessitates the simultaneous consideration of multiple criteria, reflecting the multifaceted nature of NLG tasks and outputs. This complexity arises because no single evaluation metric can adequately capture all aspects of the generated text's quality, such as coherence, informativeness, fluency, and relevance. As highlighted by the work in "Perturbation CheckLists for Evaluating NLG Evaluation Metrics," finding a single metric that correlates effectively with human scores across various dimensions is inherently challenging.

To grasp this challenge, it is crucial to acknowledge the diverse contexts and tasks in which NLG systems operate, each with distinct requirements and desired outcomes. For example, summarization tasks aim to distill large volumes of text into concise summaries that retain key information and readability [10]. Conversely, dialogue generation systems must maintain context and produce responses that are appropriate, engaging, and contextually relevant [11]. These differing objectives illustrate why a universal metric may struggle to encompass all nuances and subtleties present in NLG outputs.

The complexity intensifies when considering the intricacies of NLG outputs. The "Perturbation CheckLists for Evaluating NLG Evaluation Metrics" illustrates that a single metric cannot reliably correlate with human scores across different dimensions. For instance, a metric excelling at measuring fluency may falter in assessing informativeness or coherence. Similarly, a metric highly correlated with human judgments in one domain, such as summarization, may perform poorly in another, like dialogue generation. This observation underscores the need for a more comprehensive evaluation framework that can holistically assess NLG systems.

One reason for the failure of a single metric to cover multiple criteria is the intrinsic nature of NLG tasks. Different tasks demand varying levels of creativity, adherence to context, and faithfulness to source material. For example, abstractive summarization requires synthesizing information from source text to create coherent summaries, while question generation aims to produce relevant and answerable questions [6]. These divergent requirements necessitate the use of multiple metrics to capture the unique attributes of each task.

Moreover, the reliance on traditional metrics like BLEU and ROUGE, which focus on surface-level features such as n-gram overlap, further limits the ability to capture multifaceted criteria. Though these metrics quantify similarity between generated and reference texts efficiently, they often fail to evaluate deeper semantic and pragmatic aspects. The work in "Best Practices for Data-Efficient Modeling in NLG How to Train Production-Ready Neural Models with Less Data" highlights the inadequacies of these metrics in handling more complex NLG tasks, such as dialogue generation and question answering. These tasks require metrics that assess the quality of generated texts based on their contextual appropriateness, relevance, and informativeness, beyond mere syntactic alignment.

Addressing the multifaceted criteria assessment challenge involves developing more sophisticated evaluation frameworks that integrate a broad array of metrics tailored to specific tasks and criteria. A promising approach includes hybrid metrics combining multiple traditional and novel metrics for a more comprehensive evaluation. For instance, integrating metrics like BERTScore, which evaluates semantic similarity through contextual embeddings, alongside traditional metrics like ROUGE, can provide a more balanced assessment of generated text quality. These hybrid metrics capture both surface-level and deeper semantic features, offering a more holistic view.

Specialized metrics designed for specific NLG tasks are another viable strategy. For example, in dialogue generation, metrics like Perplexity and Self-BLEU assess fluency and response diversity, respectively, while COPIED and COPYNET gauge adherence to source material, critical in tasks like machine translation and data-to-text generation [6]. By focusing on specific criteria, these metrics offer more accurate and meaningful evaluations.

Utilizing large language models (LLMs) in NLG evaluation also holds promise. With extensive training on vast textual corpora, LLMs can generate nuanced and contextually informed evaluations. For instance, LLMs can evaluate multiple aspects of generated text, including coherence, relevance, and informativeness. However, relying on LLMs introduces challenges, such as ensuring the robustness and fairness of these evaluations.

In conclusion, the simultaneous evaluation of NLG systems based on multiple criteria presents significant challenges due to the diverse nature of NLG tasks and the multifaceted characteristics of generated texts. The limitations of traditional metrics and the insufficiency of a single metric to cover all dimensions highlight the need for more comprehensive evaluation frameworks. These frameworks should integrate a range of metrics tailored to specific tasks and criteria, as well as advanced techniques like hybrid metrics and LLMs. Addressing these challenges is crucial for advancing the field of NLG and ensuring that evaluation metrics genuinely reflect the quality and effectiveness of NLG systems.

### 2.3 In-Context Learning Limitations

In recent years, the advent of large language models (LLMs) has facilitated the development of in-context learning (ICL) approaches for evaluating natural language generation (NLG) systems. These ICL approaches enable models to learn directly from input-output pairs, potentially streamlining the evaluation process without the need for explicit training on evaluation datasets. However, while promising, ICL approaches face notable limitations, particularly concerning their robustness and generalizability across multi-dimensional NLG evaluations.

One significant limitation of ICL-based evaluations is their susceptibility to overfitting on specific datasets or task formulations. As noted in "Unveiling LLM Evaluation Focused on Metrics Challenges and Solutions," ICL models learn from a limited set of examples and may not generalize well to unseen data or tasks. If the dataset used for learning is biased or lacks diversity, the ICL model’s evaluations might be skewed, leading to unreliable assessments. For instance, a summary generated by an NLG system could appear fluent and coherent but lack informativeness or relevance to the original document—a nuance that ICL models may miss without explicit training on these specific criteria.

Moreover, ICL approaches struggle with capturing the multifaceted nature of NLG evaluations. Unlike traditional reference-based metrics that often focus on surface-level features like word overlap or sentence structure, ICL models aim to mimic human judgments by considering a broader array of attributes. However, the complexity of NLG outputs means that a single evaluation framework may not fully capture all relevant dimensions. For example, a summary could be fluent and coherent but fail to convey essential information or remain relevant to the source text. Such nuances are challenging for ICL models to discern without explicit training on these specific criteria, leading to incomplete or misleading assessments.

Another critical limitation of ICL approaches lies in their capacity to handle task variations and adaptability. NLG tasks are inherently diverse, ranging from text summarization and dialogue generation to image captioning and question generation. Each task requires a unique set of skills and criteria for evaluation, complicating the application of a uniform ICL model. While LLMs demonstrate versatility across various NLP tasks, their performance in ICL settings can vary significantly, indicating the need for specialized training or fine-tuning for each NLG task. This task-specific requirement hinders the generalizability of ICL approaches, making them less suitable for multi-task evaluation scenarios.

Furthermore, the reliance on human-generated inputs poses another challenge for ICL-based evaluations. Collecting accurate and representative human annotations can be labor-intensive and costly, particularly for complex tasks requiring nuanced judgments. Even with carefully curated datasets, human annotators may introduce biases or inconsistencies, affecting the reliability of the ICL model’s evaluations. The study "Is Reference Necessary in the Evaluation of NLG Systems When and Where" highlights the variability in human judgments and the impact of these differences on evaluation outcomes. Inconsistent annotations can lead to suboptimal learning for ICL models, resulting in evaluations that do not accurately reflect the true quality of NLG outputs.

Addressing these limitations necessitates a multifaceted approach that integrates the strengths of ICL with complementary evaluation methods. One potential solution involves hybrid frameworks that combine ICL with human evaluations, as explored in "Perturbation CheckLists for Evaluating NLG Evaluation Metrics." By leveraging human insights alongside machine learning, these hybrid models can better capture the nuances and complexities of multi-dimensional NLG evaluations. Another avenue for improvement is the development of more robust ICL models that are trained on a wider variety of data and incorporate mechanisms for mitigating bias and promoting fairness. Additionally, ongoing research in interpretability and explainability of LLMs could enhance the transparency of ICL evaluations, enabling users to better understand the basis of evaluation decisions.

In conclusion, while in-context learning holds promise for enhancing the efficiency and flexibility of NLG evaluations, it faces significant challenges in achieving robust and generalized multi-dimensional assessments. These limitations underscore the need for continued research and innovation in evaluation methodologies, with a focus on integrating diverse approaches and addressing the unique demands of NLG tasks. By doing so, the field can develop more comprehensive and reliable evaluation frameworks that truly reflect the quality and effectiveness of NLG systems.

### 2.4 Robustness Against Perturbations

Robustness Against Perturbations

Ensuring the robustness of evaluation metrics against perturbations is a critical consideration in the evaluation of NLG systems. Perturbations, defined as deliberate modifications to the inputs or outputs of the system, can significantly affect the performance characteristics of NLG models. These perturbations serve as a means to evaluate the resilience of automatic evaluation metrics under adversarial conditions, thus providing insight into their reliability and effectiveness in real-world scenarios. The robustness of current evaluation metrics under such conditions has garnered significant attention, as evidenced by the increasing recognition of the need for metrics that can withstand targeted attacks while maintaining their integrity.

The advent of large language models (LLMs) has added new dimensions to this evaluation landscape. Although LLMs are powerful tools for both generating and evaluating text, they also present substantial challenges due to their tendency to produce logically consistent yet factually inaccurate responses, often termed "hallucinations." These hallucinations can range from minor factual errors to severe logical inconsistencies, highlighting the complexity in ensuring the reliability of evaluation metrics in adversarial environments.

Several studies have explored the robustness of NLG evaluation metrics in adversarial settings. For example, the work "Are LLM-based Evaluators Confusing NLG Quality Criteria?" underscores the vulnerability of LLM-based evaluators to targeted perturbations. This study reveals that LLM-based evaluators may unintentionally confuse different quality criteria, leading to inconsistent and unreliable evaluation outcomes. Consequently, there is a pressing need for metrics capable of distinguishing subtle differences in quality dimensions and remaining robust under adversarial conditions.

To assess the robustness of evaluation metrics, it is essential to investigate how they respond to various types of perturbations. Input data perturbations, such as introducing false or misleading information, can distort the output, leading to incorrect or irrelevant text generation. The robustness of evaluation metrics can be gauged by their ability to detect and penalize deviations from expected output quality. Similarly, perturbations involving modifications to the output, such as adding noise or deliberate errors, can challenge the evaluator's capability to differentiate between accurate and inaccurate content.

Existing evaluation metrics often struggle to maintain their efficacy under adversarial conditions. Metrics like BLEU and ROUGE, which rely heavily on surface-level features such as n-gram overlaps and longest common subsequences, frequently fail to identify semantically complex errors [12]. For instance, these metrics might assign high scores to outputs containing factual inaccuracies if the syntactic structure aligns closely with the reference text, thereby failing to reflect the true quality of the generated text accurately.

The use of LLMs for evaluation introduces additional layers of complexity. As noted in "Context Matters Data-Efficient Augmentation of Large Language Models for Scientific Applications," while LLMs excel at generating coherent and semantically rich text, they can also produce logically inconsistent or factually incorrect outputs. This characteristic poses significant challenges for evaluation metrics, which must effectively distinguish between coherent yet inaccurate text and genuinely high-quality outputs. Ensuring the robustness of metrics against LLM-induced perturbations is crucial for maintaining credible evaluation outcomes.

The study "Perturbation CheckLists for Evaluating NLG Evaluation Metrics" offers valuable insights into the robustness of evaluation metrics under perturbations. This research indicates that existing metrics often perform poorly when subjected to controlled perturbations designed to isolate specific quality criteria. For instance, metrics focused on assessing fluency may fail to adequately capture the impact of perturbations aimed at reducing data coverage, resulting in inconsistent evaluation results. These findings underscore the necessity for metrics that can preserve their integrity across a broad spectrum of perturbation types, ensuring a comprehensive and reliable assessment of NLG system performance.

Moreover, the robustness of evaluation metrics must address potential biases and inconsistencies introduced by LLMs. As highlighted in "Attention Satisfies A Constraint-Satisfaction Lens on Factual Errors of Language Models," LLMs exhibit variable attention to constraint tokens during generation, influencing the factual accuracy of the output. Metrics that overlook these variations may yield biased evaluations, favoring outputs from LLMs that adhere more strictly to constraints. Ensuring the robustness of metrics requires addressing such underlying biases, thereby promoting fair and consistent assessments of NLG system performance.

Beyond technical accuracy, the robustness of metrics should also provide meaningful and actionable insights for enhancing NLG systems. Robust metrics under adversarial conditions can guide developers in pinpointing specific weaknesses within their models, enabling targeted improvements. On the other hand, metrics vulnerable to perturbations might lead to misguided optimizations, potentially worsening existing issues within the NLG system. Thus, robustness is not just about reliability but is also a key driver in advancing NLG technology.

In conclusion, the robustness of NLG evaluation metrics under adversarial conditions is vital for ensuring their effectiveness and reliability. The challenges posed by perturbations highlight the need for metrics that can withstand targeted attacks, maintain their integrity, and provide valuable insights for system enhancement. By tackling these challenges, researchers and practitioners can develop more robust and comprehensive evaluation frameworks that accurately reflect the performance of NLG systems across varied and complex tasks.

### 2.5 Ethical and Practical Considerations

Ethical and practical considerations are integral to the evaluation of Natural Language Generation (NLG) systems. As highlighted in "Deconstructing NLG Evaluation Evaluation Practices, Assumptions, and Their Implications," these considerations encompass the ethical alignment of NLG systems with human values and societal norms, alongside practical concerns regarding the feasibility and reliability of evaluation methods. The primary goal of NLG evaluation is to measure the effectiveness and quality of generated text relative to specific tasks and user expectations, which requires a nuanced understanding of quality criteria that can vary significantly based on context and application.

Ethically, NLG systems must prioritize values such as patient privacy and confidentiality in healthcare settings and accuracy and fairness in news reporting or educational content generation. Evaluations must ensure that NLG outputs meet these ethical standards, avoiding the generation of harmful or misleading content. Inappropriate or biased references can result in unethical outputs, emphasizing the need for carefully curated and diverse reference sets during evaluation processes. This ensures that NLG systems uphold ethical standards and do not perpetuate biases or misinformation.

Practically, the feasibility and reliability of evaluation methods are significant concerns. Traditional metrics like BLEU and ROUGE, while widely used, face limitations in capturing the nuances of NLG outputs, particularly those requiring high levels of creativity or context understanding [1]. These metrics rely heavily on surface-level features, leading to discrepancies between automated scores and human judgments. The advent of large language models (LLMs) as evaluators presents both opportunities and challenges. While LLMs can offer more context-aware evaluations, they can also confuse different evaluation criteria, highlighting the need for rigorous validation and calibration [13].

Scalability and efficiency are also critical practical considerations. Direct human evaluation, despite being highly reliable, can be time-consuming and resource-intensive, limiting its applicability in large-scale testing scenarios. Automated metrics, while faster and more scalable, may suffer from data leakage issues when trained on similar datasets as the evaluated systems, leading to inflated performance estimates [14]. Hybrid approaches combining human evaluations with automated metrics seek to balance reliability and workload reduction. However, these methods must be carefully designed to avoid introducing biases or inconsistencies [15].

Underlying assumptions in NLG evaluations include the belief that a single metric can comprehensively assess quality, which the multifaceted nature of NLG tasks challenges [5]. This necessitates a holistic evaluation approach considering multiple dimensions of quality, such as fluency, coherence, relevance, and adequacy, each requiring different evaluation methods and criteria.

Resource constraints, such as the availability of annotated datasets and skilled human evaluators, can limit evaluation effectiveness. The reliance on limited or biased datasets can skew results, making generalization to different contexts challenging. Technological advancements in deep learning and multimodal processing further complicate the applicability of existing evaluation methods, underscoring the need for continuous adaptation and robust evaluation practices.

Finally, the broader societal impacts of NLG systems must be considered. Deployments in fields like journalism or education can profoundly influence public discourse and knowledge dissemination. Evaluations must account for these impacts, ensuring positive contributions to served communities. Potential misuses, such as in disinformation campaigns or malicious content generation, highlight the importance of robust evaluation practices promoting ethical use and responsibility.

Addressing these ethical and practical considerations is crucial for ensuring the reliability, validity, and societal benefit of NLG systems. A multidimensional, context-aware evaluation approach integrating diverse methods and continuously adapting to technological advancements can enhance system development and foster more informed and ethical AI applications across domains.

## 3 Traditional Metrics Overview

### 3.1 Overview of Reference-Based Metrics

Reference-based metrics have long served as the cornerstone for evaluating the effectiveness of Natural Language Generation (NLG) systems. Among these, two prominent metrics, BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation), stand out due to their widespread adoption and relative ease of computation. These metrics, however, are fundamentally based on the premise of comparing the generated text against a set of reference texts, often crafted by human annotators, to gauge the fidelity and quality of the output.

BLEU, introduced by Papineni et al., operates on the principle of n-gram precision. It computes the overlap between the generated text and the reference texts by calculating the precision of n-grams (sequences of n consecutive words) present in both the generated and reference texts. The metric is calculated as the geometric mean of the modified n-gram precisions, where the modification accounts for the brevity penalty, a term that penalizes shorter generated texts compared to the reference. BLEU is commonly applied in machine translation and summarization tasks, where it serves as a benchmark for assessing the syntactic correctness and lexical diversity of generated texts. Despite its popularity, BLEU faces significant limitations, particularly in its inability to capture semantic nuances, coherence, and the overall communicative effectiveness of the generated text.

ROUGE, developed by Lin and Hovy, takes a slightly different approach by focusing on recall rather than precision. ROUGE measures the similarity between the generated text and the reference texts by calculating the longest common subsequence of words, phrases, or n-grams between them. ROUGE offers several variants, including ROUGE-N, which considers n-gram overlap, ROUGE-L, which evaluates the longest common subsequence, and ROUGE-S, which focuses on skip-bigram overlap. Similar to BLEU, ROUGE is extensively used in tasks such as summarization and machine translation, providing a straightforward means to evaluate the informativeness and coverage of generated texts. While ROUGE improves upon BLEU by considering the longest common subsequences, it too falls short in capturing deeper semantic and contextual meanings, leading to its inadequacy in fully reflecting the quality of NLG outputs.

Despite their limitations, BLEU and ROUGE remain popular due to their simplicity and computational efficiency. However, recent advancements in Natural Language Processing (NLP) have revealed the inadequacies of these metrics in evaluating more complex and creative NLG tasks. For instance, as noted in [3], traditional metrics struggle to effectively evaluate the outputs of Large Language Models (LLMs), which are capable of generating highly diverse and contextually rich texts. This inadequacy stems from the fact that these metrics heavily rely on surface-level features, failing to account for deeper semantic and pragmatic aspects that are crucial for assessing the quality of NLG outputs.

Moreover, the reliance on human-generated references introduces another layer of complexity. Collecting high-quality references requires substantial human effort and can be biased, particularly when dealing with tasks that involve subjective judgments. This issue is exacerbated in scenarios where the output space is vast and varied, making it challenging to gather representative references. Consequently, the quality and reliability of reference-based metrics are directly tied to the quality of the references themselves, leading to inconsistencies in evaluation outcomes across different datasets and tasks.

To address these limitations, researchers have explored alternative evaluation methods that do not rely on explicit references. One such approach involves the use of large language models (LLMs) for generating evaluations without human references. These models, trained on extensive corpora, can provide nuanced assessments of NLG outputs by leveraging their understanding of language and context. Another approach involves developing machine-learned metrics that are trained on annotated data to evaluate NLG outputs independently of references. These methods offer promising avenues for overcoming the limitations of traditional reference-based metrics, paving the way for more comprehensive and context-aware evaluation of NLG systems.

In conclusion, while reference-based metrics like BLEU and ROUGE continue to play a significant role in NLG evaluation, their inherent limitations underscore the need for more sophisticated and context-aware evaluation methods. The rise of LLMs and machine-learned metrics represents a step forward in this direction, offering new possibilities for enhancing the accuracy and reliability of NLG evaluations. As the field of NLG continues to evolve, so too must the methodologies used to evaluate its outputs, necessitating ongoing research and innovation in evaluation metrics.

### 3.2 Methodologies and Limitations of BLEU and ROUGE

BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are widely adopted metrics in the evaluation of NLG systems, particularly in tasks such as machine translation and text summarization. These metrics have been instrumental in providing quantitative measures of the similarity between generated text and reference texts. However, despite their widespread adoption, they suffer from significant limitations, especially when it comes to capturing the nuances, semantic understanding, and relevance to human judgments.

Initially designed for machine translation, BLEU employs a precision-based scoring method to measure the overlap between the candidate text and one or more reference texts. It computes the n-gram overlap, typically up to four grams, between the candidate text and reference texts, and then applies a brevity penalty to account for length differences. BLEU’s primary advantage lies in its simplicity and computational efficiency, making it suitable for large-scale evaluations. For instance, in summarization tasks, BLEU scores indicate the extent to which a generated summary overlaps with a reference summary, highlighting shared key phrases or sentences [10].

However, BLEU's reliance on n-gram overlap inherently limits its ability to capture more abstract linguistic phenomena. One of its key limitations is its incapacity to account for semantic coherence and contextual appropriateness. For example, BLEU scores might be high even if the generated text includes irrelevant or nonsensical content, as long as it matches the n-grams in the reference texts [10]. This limitation becomes particularly evident in tasks like dialogue generation and question generation, where the semantic coherence and context sensitivity of the generated text are paramount. BLEU’s overemphasis on surface-level agreement can lead to inflated scores for text that fails to convey the intended meaning, thereby undermining its utility in these domains.

Unlike BLEU, ROUGE focuses on recall, measuring the longest common subsequence (LCS) between the generated text and the reference text. ROUGE includes several variants, such as ROUGE-1 (unigram LCS), ROUGE-2 (bigram LCS), and ROUGE-L (longest LCS). ROUGE’s reliance on LCS allows it to capture longer sequences of words, which can be advantageous in certain tasks, particularly those involving the identification of longer coherent phrases or sentences [10].

Despite its strengths, ROUGE shares similar limitations with BLEU in terms of its inability to fully capture semantic coherence and context sensitivity. Like BLEU, ROUGE struggles to differentiate between text that aligns superficially with the reference text and text that genuinely conveys the intended meaning. For example, in summarization tasks, a generated summary might score highly on ROUGE if it contains many phrases found in the reference summary, even if the summary lacks the overall coherence and relevance expected of a good summary [10]. This limitation is exacerbated in more complex NLG tasks, where the alignment between generated text and reference text might not be sufficient to ensure high-quality outputs.

Both BLEU and ROUGE face additional challenges in evaluating the creativity and diversity of generated text. Creativity in NLG often involves producing text that deviates from the literal wording of the reference text while still conveying the intended meaning. Metrics like BLEU and ROUGE, which are based on exact word matching, struggle to reward such creative deviations. Similarly, the diversity of NLG outputs, crucial for tasks like question generation and dialogue generation, is not well captured by these metrics. Generated questions, for instance, might vary widely in terms of phrasing and style, yet still convey the same underlying information. Traditional metrics like BLEU and ROUGE might penalize such variation, leading to inaccurate assessments of the generated text's quality [10].

Moreover, the reliance of BLEU and ROUGE on reference texts poses significant limitations. These metrics assume the availability of high-quality reference texts, which might not always be feasible or reliable. In many NLG applications, generating appropriate reference texts can be labor-intensive and subjective, potentially introducing biases into the evaluation process. The quality of the reference texts can also vary, affecting the validity of the metrics [10]. Additionally, the choice of reference texts can influence the scores obtained, making it difficult to compare evaluations across different studies or tasks.

In conclusion, while BLEU and ROUGE have played a pivotal role in the evaluation of NLG systems, their reliance on n-gram overlap and longest common subsequence introduces significant limitations. These metrics struggle to capture the semantic coherence, context sensitivity, and creativity of generated text, which are essential for accurate and meaningful evaluations. The reliance on reference texts further complicates the evaluation process, limiting the applicability and reliability of these metrics in certain contexts. As the field of NLG continues to evolve, there is a pressing need for the development of more sophisticated and comprehensive evaluation metrics that can effectively address these limitations [10].

### 3.3 Challenges in Applying Traditional Metrics

Traditional reference-based metrics such as BLEU and ROUGE have long been staples in the evaluation of Natural Language Generation (NLG) systems, primarily due to their simplicity and ease of computation. However, these metrics are increasingly encountering significant challenges when applied to the assessment of diverse and creative NLG outputs. This section explores these challenges, focusing on issues related to data leakage, reference diversity, and the limitations of traditional metrics in capturing the nuances of open-ended generation tasks.

One of the primary challenges in using traditional metrics is data leakage. Data leakage occurs when information from outside the training dataset is used in the model evaluation, leading to inflated performance estimates. In the context of NLG, this can happen when the same text segments used during training are inadvertently included in the test data or reference texts. This scenario leads to overly optimistic evaluation scores, as the model is essentially being tested on content it has already seen. This issue is particularly problematic in creative tasks where the model is expected to generate highly varied and original content; any overlap with training data can artificially inflate scores, misleading developers about the true capabilities and limitations of the NLG system.

Another critical challenge is reference diversity. Traditional metrics rely heavily on predefined reference texts to evaluate the quality of generated outputs. This poses a significant limitation as the ideal reference text can vary widely depending on the specific task and the nature of the generated text. Generating a diverse set of reference texts that can fully capture the variability and complexity of human language is a formidable task. The lack of diversity in reference texts can lead to biased evaluation results, favoring outputs that closely resemble the limited set of provided references. This issue is particularly pronounced in tasks requiring creative or open-ended responses, where a single reference may not adequately represent the wide spectrum of acceptable outputs.

Additionally, traditional metrics often fall short in capturing the nuances and subtleties of open-ended generation tasks. Tasks such as dialogue generation or story writing require models to produce text that is not only grammatically correct and coherent but also contextually appropriate and engaging. Traditional metrics like BLEU and ROUGE, which primarily focus on lexical overlap and sequence similarity, may miss out on the higher-order linguistic properties crucial for assessing the quality of such outputs. For example, a generated sentence might achieve high BLEU scores if it matches the reference text in terms of n-grams, yet it could still be semantically incorrect or pragmatically inappropriate. This discrepancy underscores the need for more sophisticated evaluation metrics that can account for broader context and the intended purpose of the generated text.

Moreover, the reliance on fixed reference texts can limit the flexibility and adaptability of NLG systems. In dynamic and interactive environments, such as chatbots or virtual assistants, generated text should adapt to changing contexts and user inputs. Traditional metrics grounded in static reference texts may not handle the variability and unpredictability inherent in such settings. This limitation is evident in tasks requiring real-time interaction and adaptation, where generated text must align with ongoing conversations and user preferences. Consequently, traditional metrics might undervalue the importance of contextual appropriateness and adaptability—key attributes of successful NLG systems in interactive applications.

The advent of large language models (LLMs) has introduced additional complexities to NLG evaluation. While LLMs excel at generating coherent and contextually relevant text, evaluating their outputs requires metrics that capture the depth and breadth of their capabilities. Traditional metrics, with their focus on surface-level features, may not suffice to assess the true quality and effectiveness of LLM-generated text.

Finally, task specificity poses another significant challenge for traditional metrics. Different NLG tasks, such as text summarization, image captioning, and dialogue generation, have distinct requirements and evaluation criteria. While traditional metrics like BLEU and ROUGE are broadly applicable, they may not be equally effective across all tasks. For instance, in question generation, where the goal is to produce questions that are both relevant and answerable, traditional metrics focused solely on textual similarity might overlook the functional aspect of generated questions. Similarly, in dialogue generation, the quality of generated text depends not only on lexical correctness but also on maintaining conversational flow and engaging users. Capturing these nuances using traditional metrics is difficult, as they often prioritize surface-level agreement over deeper semantic and pragmatic considerations.

In conclusion, while traditional metrics like BLEU and ROUGE remain valuable tools for NLG evaluation, their application to diverse and creative outputs presents several challenges. Issues such as data leakage, reference diversity, and the limitations in capturing nuanced open-ended generation tasks highlight the need for more sophisticated and adaptable evaluation methods. As NLG systems continue to evolve and integrate into dynamic and interactive environments, developing comprehensive and context-aware evaluation metrics will be essential for accurately assessing and improving system performance. The growing recognition of these limitations underscores the importance of ongoing research into new and innovative evaluation approaches that can better capture the full spectrum of NLG system capabilities and outputs.

### 3.4 Comparative Analysis with Novel Metrics

Traditional reference-based metrics such as BLEU and ROUGE have been widely adopted in the field of Natural Language Generation (NLG) due to their simplicity and ease of computation. However, these metrics often struggle with capturing the semantic nuances and contextual richness inherent in NLG outputs, leading researchers to develop novel metrics like BERTScore and MARS. These newer metrics aim to address the limitations of traditional metrics by leveraging more advanced language understanding models and sophisticated evaluation techniques. This subsection provides a comparative analysis of these traditional and novel metrics, highlighting the improvements and enhancements offered by the latter in evaluating NLG systems.

**Traditional Metrics: BLEU and ROUGE**

BLEU (Bilingual Evaluation Understudy) is a metric that measures the similarity between a machine-generated sentence and a set of human-generated reference sentences by calculating the n-gram overlap between them. It assigns higher scores to generated sentences that closely resemble the reference sentences in terms of word sequences. ROUGE (Recall-Oriented Understudy for Gisting Evaluation), on the other hand, focuses on the recall aspect, measuring the overlap between the generated text and the reference summaries. While these metrics are straightforward and computationally efficient, they often fail to capture the semantic meaning and contextual appropriateness of the generated text.

**Novel Metrics: BERTScore and MARS**

BERTScore is a metric that evaluates the similarity between the generated text and reference text using embeddings from BERT, a state-of-the-art transformer-based model. Unlike BLEU and ROUGE, which rely on surface-level features like word sequences, BERTScore leverages the deep semantic understanding capabilities of BERT to provide a more accurate assessment of text quality. By comparing the embeddings of the generated and reference texts, BERTScore captures the semantic meaning and context, offering a more nuanced evaluation of NLG outputs. Similarly, MARS (Multi-reference Automatic Relevance Score) is another metric that utilizes the embeddings from pre-trained language models to evaluate the relevance and coherence of generated texts relative to multiple reference texts. MARS addresses the issue of reference diversity by incorporating multiple references, making it more robust and reliable in evaluating NLG systems.

**Improvements Offered by Novel Metrics**

One of the primary improvements offered by BERTScore and MARS is their ability to capture semantic meaning and context. Unlike BLEU and ROUGE, which primarily focus on surface-level features like n-gram overlap and recall, BERTScore and MARS leverage deep language understanding models to provide a more comprehensive evaluation. This allows these metrics to assess the relevance and coherence of generated texts in a way that aligns more closely with human judgments. Furthermore, BERTScore and MARS can handle the diversity and complexity of NLG outputs more effectively, addressing the limitations of traditional metrics in evaluating creative and open-ended generation tasks.

**Comparative Analysis**

When comparing BERTScore and MARS with traditional metrics like BLEU and ROUGE, it becomes evident that the newer metrics offer several advantages. For instance, BERTScore has been shown to outperform BLEU and ROUGE in various NLG tasks, such as text summarization and image captioning, by capturing the semantic meaning and context of the generated texts. According to a comprehensive benchmark study on NLG evaluation metrics, BERTScore demonstrated superior performance in evaluating the quality of generated texts compared to traditional metrics [16]. This indicates that BERTScore is better equipped to assess the semantic richness and contextual appropriateness of NLG outputs.

Similarly, MARS has been found to provide more reliable and consistent evaluations across multiple reference texts, addressing the issue of reference diversity that plagues traditional metrics. By incorporating multiple references, MARS ensures that the evaluation is not biased towards any single reference and reflects the overall quality of the generated text more accurately. A comparative study of various NLG evaluation metrics found that MARS outperformed traditional metrics in evaluating the relevance and coherence of generated texts across different tasks and domains [16].

Moreover, BERTScore and MARS have shown greater robustness against perturbations and adversarial conditions compared to traditional metrics. Traditional metrics like BLEU and ROUGE tend to struggle when faced with perturbations or changes in the input context, often producing inconsistent evaluations. In contrast, BERTScore and MARS, due to their reliance on deep language understanding models, are more resilient to such variations and provide more stable evaluations [5]. This makes them more suitable for evaluating NLG systems under diverse and challenging conditions.

**Challenges and Limitations**

Despite the significant improvements offered by BERTScore and MARS, these metrics also come with their own set of challenges and limitations. One of the major challenges is the computational cost associated with using pre-trained language models like BERT for evaluation. BERTScore and MARS require substantial computational resources for generating and comparing embeddings, making them less practical for real-time or resource-constrained applications. Additionally, the performance of these metrics heavily depends on the quality and relevance of the pre-trained language models used. If the language model is not well-suited for the specific NLG task or domain, the evaluation results may be compromised.

Another limitation is the potential for bias in the evaluation process. Pre-trained language models like BERT are trained on large corpora of text data and may inherit biases present in the training data. This could lead to biased evaluations if the generated texts contain elements that align with the biases present in the language model. Furthermore, the evaluation results may be influenced by the specific choices made during the training and fine-tuning of the language model, introducing additional variability into the evaluation process.

In conclusion, while traditional metrics like BLEU and ROUGE continue to play a crucial role in NLG evaluation, the emergence of novel metrics such as BERTScore and MARS offers significant improvements in capturing semantic meaning and contextual richness. These newer metrics provide a more comprehensive and nuanced evaluation of NLG outputs, addressing the limitations of traditional metrics in evaluating diverse and complex NLG tasks. However, the adoption of these novel metrics also comes with challenges and limitations that need to be carefully considered. Future research should focus on further refining these metrics and addressing their limitations to ensure they become more practical and reliable tools for NLG evaluation.

### 3.5 Specific Applications and Limitations

Traditional metrics like BLEU and ROUGE, despite their widespread use, exhibit notable limitations when applied to specific NLG tasks such as question generation, summarization, and dialogue generation. These limitations arise due to the unique challenges and requirements inherent in these tasks that traditional metrics fail to adequately address. In the following sections, we will delve into these specific applications and highlight the limitations of traditional metrics in each context.

**Question Generation**

Question generation (QG) involves creating questions based on a given text or topic. Traditional metrics like BLEU and ROUGE, which are primarily designed to measure lexical overlap and syntactic similarity, are less suited for assessing the quality of generated questions. BLEU and ROUGE were originally developed for tasks like translation and summarization, where the goal is to produce a coherent and fluent text that closely mirrors a human reference. However, in QG, the generated output must not merely replicate the input text but rather transform it into meaningful questions. As noted in 'Why We Need New Evaluation Metrics for NLG', traditional metrics fail to capture the nuances required for QG tasks because they do not account for the structure and intent of questions.

One significant limitation of using BLEU and ROUGE for QG is their inability to assess the answerability of generated questions. Answerability refers to the degree to which a question can be answered correctly using the provided context or knowledge. Traditional metrics do not inherently understand whether a question can be answered accurately, which is a crucial criterion in QG. To address this, PMAN was introduced as a specialized metric for QG that specifically evaluates the answerability of generated questions [1]. PMAN utilizes a set of predefined answerable and non-answerable pairs to score the generated questions, offering a more precise evaluation compared to traditional metrics.

Another challenge lies in the evaluation of question quality in terms of informativeness and complexity. Traditional metrics are often insufficient in distinguishing between questions that are superficially similar yet vary significantly in terms of their cognitive load and information content. This discrepancy highlights the need for metrics that can capture the semantic and pragmatic aspects of questions, something that traditional metrics struggle to achieve. As discussed in 'Perturbation CheckLists for Evaluating NLG Evaluation Metrics', the multifaceted nature of NLG evaluation underscores the inadequacy of single-criteria metrics like BLEU and ROUGE, which tend to overlook critical dimensions such as question complexity and informativeness [5].

**Summarization**

Text summarization is another area where traditional metrics face significant limitations. Summarization tasks require the extraction or generation of a concise version of a longer document, preserving key information and maintaining coherence. Traditional metrics like ROUGE, which rely heavily on n-gram overlaps, can inadvertently favor summaries that include repetitive or irrelevant content, as long as they match the reference summary at the lexical level. This tendency to reward surface-level similarities over semantic coherence can lead to misleadingly high scores for suboptimal summaries [17].

Moreover, traditional metrics often struggle to handle the variability in summarization tasks, particularly when dealing with open-domain texts. Different summaries of the same text can vary widely in terms of structure, vocabulary, and tone, making it challenging for metrics like ROUGE to provide a fair and comprehensive evaluation. As highlighted in 'Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluation', the reliance on a single reference can result in biased and unreliable evaluations, especially when the reference does not capture the full range of possible summaries [14]. To mitigate this issue, the introduction of multiple references has shown promise in enhancing the reliability of evaluation metrics [14].

Furthermore, the evaluation of summarization quality often extends beyond mere lexical overlap to include aspects such as readability, coherence, and informativeness. Traditional metrics are inadequate in capturing these dimensions, leading to the development of more sophisticated evaluation frameworks. For instance, the work presented in 'Evaluation Discrepancy Discovery  A Sentence Compression Case-study' emphasizes the need for comprehensive evaluation protocols that go beyond surface-level metrics to assess the true quality of generated summaries [18]. The discovery that a system can achieve high scores on traditional metrics while producing subpar summaries underscores the limitations of these metrics in reflecting human judgments accurately.

**Dialogue Generation**

Dialogue generation tasks pose yet another set of challenges for traditional metrics. In dialogue systems, the generated responses should be contextually relevant, engaging, and maintain a consistent character persona or conversational style. Traditional metrics, designed primarily for monologue generation tasks, often fail to capture the nuances of interactive dialogue. BLEU and ROUGE, which focus on lexical and structural similarity, are ill-suited for evaluating dialogue coherence, fluency, and relevance, as these aspects require a deeper understanding of the conversation context [5].

Dialogue generation systems must balance multiple objectives, including maintaining context, generating informative and engaging responses, and adapting to user behavior. Traditional metrics, being single-faceted, are unable to holistically assess these objectives. For example, a dialogue system might generate responses that are semantically correct but fail to align with the conversational context, leading to a poor user experience despite achieving high scores on traditional metrics. This discrepancy highlights the need for multi-criteria evaluation frameworks that can effectively capture the multifaceted nature of dialogue generation [5].

To address these limitations, researchers have explored the development of dialogue-specific evaluation metrics. These metrics aim to assess not just the linguistic quality of individual utterances but also their role in maintaining a coherent and engaging conversation. The emergence of large language models (LLMs) offers new opportunities for dialogue evaluation, as these models can generate evaluations that are more aligned with human judgments [13]. However, the reliance on LLMs also introduces challenges, such as the potential for confusion among different evaluation criteria and the need for careful calibration to ensure accurate evaluations [13].

**Conclusion**

In conclusion, traditional metrics like BLEU and ROUGE, while valuable, exhibit significant limitations when applied to specific NLG tasks. These limitations arise from the unique challenges and requirements of tasks such as question generation, summarization, and dialogue generation. Traditional metrics often fall short in capturing the nuanced aspects of these tasks, such as question answerability, summarization informativeness, and dialogue coherence. To address these limitations, the development of specialized metrics and evaluation frameworks is essential. As the field of NLG continues to evolve, the quest for more comprehensive and context-aware evaluation metrics remains a crucial research direction.

## 4 Reference-Free Evaluation Techniques

### 4.1 Taxonomy of Reference-Free Evaluation Methods

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Reference-free evaluation methods represent a significant advancement in the realm of Natural Language Generation (NLG) system assessment, offering a means to evaluate outputs without the need for explicit human-generated references. These methods can be categorized based on their underlying methodologies and applications within NLG systems. This section provides a structured overview of the various reference-free evaluation methods available, emphasizing their distinct characteristics and functionalities.

One primary category involves the use of machine learning techniques, specifically leveraging the predictive capabilities of pre-trained models, such as large language models (LLMs), to generate evaluations. These methods operate on the principle that if a model can predict certain aspects of the input or output, it can also infer the quality of generated text without needing direct human annotations. For instance, LLMs can be prompted with specific instructions to rate the quality of generated text based on predefined criteria, such as fluency, coherence, or relevance [2]. This approach allows for a more context-aware evaluation, as the models are trained on vast amounts of textual data and can therefore understand and assess text quality in a way that aligns with human perception.

Another notable category encompasses metrics derived directly from the generated text itself, without the need for external references. These metrics typically focus on intrinsic properties of the text, such as lexical richness, syntactic complexity, or semantic coherence. They can be particularly useful in scenarios where obtaining human references is impractical or too costly. For example, metrics like BERTScore [1] and MARS [3] leverage contextual embeddings to gauge the similarity between generated texts and human-written references. While these metrics are designed to be reference-based, they can also serve as reference-free metrics by comparing the generated text against a corpus of similar texts, allowing for an indirect assessment of text quality.

A third category involves the use of synthetic references generated by machine learning algorithms. This approach circumvents the need for human-generated references by using models trained on large datasets to generate plausible alternatives against which the NLG output can be compared. The idea is that if a machine can generate a plausible alternative, then the original human reference is likely to be equally valid. This method has been explored in the context of summarization and dialogue generation, where the availability of synthetic references has allowed for more robust and scalable evaluation frameworks [4].

In addition to these categories, there are also hybrid approaches that combine elements from multiple methodologies to create more comprehensive evaluation frameworks. One such approach involves integrating LLMs with human evaluations, leveraging the strengths of both machine learning models and human judgment. This combination can help mitigate the limitations of purely automated methods, which may struggle with certain types of text quality that are difficult to capture algorithmically. By incorporating human evaluations, these hybrid approaches can provide a more nuanced and accurate assessment of NLG system performance. For instance, control variate techniques can be employed to adjust for biases that might arise from differences in human evaluator behavior, ensuring that the final evaluation score reflects a balanced view of system performance [17].

Furthermore, the application of LLMs in reference-free evaluation extends beyond just rating text quality; they can also be used to detect and correct errors in the generated text. By prompting LLMs to identify and fix errors, one can gain insights into the types of mistakes that the NLG system is prone to making. This not only helps in improving the quality of generated text but also provides valuable feedback for model refinement and optimization. Such an approach has been demonstrated in the context of question generation, where LLMs can be used to ensure that the generated questions are both relevant and answerable [8].

Moreover, reference-free evaluation methods are particularly useful in evaluating NLG systems in less-studied domains or languages where obtaining human-generated references is challenging. These methods can adapt to new domains or languages by being trained on a diverse corpus, thus providing a flexible and scalable solution for NLG evaluation. For example, the use of LLMs trained on multilingual corpora can enable the evaluation of NLG systems across different languages, ensuring that the evaluation is not biased towards anglo-centric corpora [2].

However, despite their advantages, reference-free evaluation methods also come with their own set of challenges. One major issue is the potential for bias in the evaluations produced by LLMs. If the training data for these models is skewed or contains biases, these biases can be reflected in the evaluation scores. Additionally, the performance of these models can vary depending on the specific task and the nature of the input data, necessitating careful consideration when applying them to new or unfamiliar domains [17]. Another challenge is the difficulty in ensuring that the metrics derived from machine learning models are truly reflective of human judgments. While machine learning models can capture certain aspects of language quality, they may miss subtle nuances that are important for human readers. This highlights the need for continuous validation and calibration of these metrics to ensure they remain aligned with human perceptions of text quality [3].

In conclusion, the taxonomy of reference-free evaluation methods reveals a diverse array of approaches, each with its own strengths and limitations. From machine learning-based metrics to hybrid human-machine frameworks, these methods offer a robust toolkit for evaluating NLG systems without the need for explicit human references. As the field continues to evolve, it is likely that these methods will become increasingly sophisticated, incorporating more advanced machine learning techniques and adapting to the growing complexity of NLG tasks. The ongoing development of these methods holds the promise of more accurate, reliable, and scalable evaluation solutions, ultimately contributing to the advancement of NLG technologies.
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### 4.2 Utilizing Large Language Models for Evaluation

Utilizing Large Language Models (LLMs) for Evaluation

In recent years, the emergence of large language models (LLMs) [19; 20] has revolutionized the landscape of natural language processing (NLP) and natural language generation (NLG). One of the most significant impacts of LLMs lies in their transformative role in the automatic evaluation of NLG systems. Traditionally, NLG evaluation has relied heavily on reference-based metrics that compare generated texts against predefined reference texts. However, such metrics often fail to capture the nuances and complexities inherent in NLG outputs, particularly in tasks that demand creativity and flexibility. This limitation is especially pronounced in the absence of human-generated references, where reference-based metrics falter due to their reliance on static and potentially biased reference texts. LLMs, on the other hand, offer a promising alternative by enabling reference-free evaluations that are grounded in the model's understanding of language and context.

At the core of LLM-based evaluations is the ability of these models to generate evaluations autonomously, without the need for human-provided references. This capability stems from the extensive pre-training of LLMs on vast corpora of textual data, allowing them to acquire a deep understanding of linguistic structures, semantics, and pragmatic considerations. Consequently, LLMs can be harnessed to generate evaluations that reflect a comprehensive understanding of the NLG outputs, capturing aspects such as coherence, informativeness, and stylistic appropriateness. For instance, a study on the evaluation of abstractive summarization tasks demonstrated that LLMs could generate evaluations that aligned closely with human judgments, showcasing the potential of LLMs to serve as reliable evaluators in the absence of explicit references [6].

A key advantage of LLM-based evaluations is their adaptability to different NLG tasks and domains. Unlike traditional reference-based metrics, which are often task-specific and require tailored reference sets, LLMs can be fine-tuned or adapted to evaluate NLG outputs across a wide spectrum of tasks, from text summarization to dialogue generation. This flexibility is particularly valuable in scenarios where the generation of human references is impractical or costly, such as in specialized domains or for emergent language phenomena. By leveraging the contextual understanding and generative capabilities of LLMs, evaluators can obtain comprehensive assessments that go beyond surface-level features, providing deeper insights into the quality and effectiveness of NLG systems.

Moreover, LLMs offer a robust solution to the challenge of multi-criteria assessment in NLG evaluations. Traditional metrics often struggle to evaluate NLG systems based on multiple criteria simultaneously [5]. For example, a system might excel in generating coherent and informative summaries but fall short in terms of stylistic variety or emotional tone. LLM-based evaluations can overcome this limitation by integrating multiple evaluation criteria within a unified framework. By fine-tuning LLMs on datasets that emphasize different evaluation dimensions, evaluators can generate assessments that reflect the overall quality of the NLG outputs, taking into account a wide range of factors such as fluency, coherence, relevance, and creativity. This comprehensive approach ensures that the evaluations provided by LLMs are more reflective of the holistic performance of NLG systems.

However, while LLMs hold great promise for NLG evaluations, their adoption is not without challenges. One significant issue is the potential for biases and inaccuracies in the evaluations generated by LLMs. As noted in "Are LLM-based Evaluators Confusing NLG Quality Criteria," large language models can sometimes generate evaluations that are influenced by internal biases or fail to align with human judgments [13]. These biases can stem from the underlying data distributions used during pre-training or from the fine-tuning processes that may inadvertently introduce skewed perspectives. To mitigate these risks, it is crucial to carefully design and validate the training and evaluation protocols used for LLMs. This includes employing diverse and representative datasets, implementing robust validation schemes, and conducting thorough analyses to ensure the reliability and fairness of the generated evaluations.

Additionally, the computational resources required for LLM-based evaluations represent another significant consideration. Training and fine-tuning large language models are resource-intensive processes that demand substantial computational power and storage capacity. While recent advancements in model architectures and training methodologies have made it possible to deploy LLMs more efficiently, the practical constraints associated with these models remain a concern. Researchers and practitioners need to weigh the benefits of LLM-based evaluations against the logistical challenges, ensuring that the adoption of these models is both feasible and cost-effective.

Despite these challenges, the potential of LLMs for NLG evaluations remains a compelling area of exploration. By combining the strengths of LLMs with human insights and expertise, evaluators can develop comprehensive frameworks that leverage the best of both worlds. For instance, hybrid approaches that utilize LLMs to generate initial evaluations, followed by human reviews and refinements, can yield more nuanced and accurate assessments that reflect the multifaceted nature of NLG tasks. Such an approach not only leverages the scale and depth of knowledge offered by LLMs but also incorporates human judgment to ensure evaluations are finely tuned to reflect the intended goals of the NLG system.

In summary, the role of LLMs in the automatic evaluation of NLG systems represents a significant leap forward in the quest for effective and comprehensive evaluation metrics. By harnessing the contextual understanding and generative capabilities of LLMs, evaluators can generate assessments that capture the complexities and nuances inherent in NLG outputs, providing deeper insights into the quality and effectiveness of NLG systems. As the field continues to evolve, the integration of LLMs with other evaluation methods will likely play a crucial role in advancing the state of NLG evaluation, fostering more accurate and reliable assessments that drive the continued progress of NLG research and applications.

### 4.3 Machine-Learned Metrics for NLG Evaluation

Machine-learned metrics for NLG evaluation represent a significant advancement in the field, enabling the assessment of generated text without the dependency on human-generated references. Building on the capabilities of large language models (LLMs) [7], these metrics harness machine learning algorithms to provide more nuanced and context-aware evaluations. The core principle behind these metrics is their ability to learn from large volumes of text data, capturing a broader range of linguistic patterns and characteristics essential for NLG output evaluation.

One notable approach involves training machine-learned metrics on extensive datasets to recognize high-quality text generation. These models can then predict the quality of a given piece of generated text based on learned representations of good and bad examples. For instance, the work on "Leveraging Large Language Models for NLG Evaluation" demonstrates the utility of training LLMs to assess NLG outputs based on criteria such as fluency, coherence, relevance, and informativeness. Such metrics are critical for tasks where traditional metrics like BLEU or ROUGE fall short due to their reliance on explicit reference texts.

Another key aspect of machine-learned metrics is their adaptability to different NLG tasks and domains. Unlike traditional reference-based metrics that often struggle with inconsistency across varied tasks [17], machine-learned metrics can be fine-tuned or generalized to accommodate the nuances of specific NLG applications. An illustrative example is the "DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering," which introduces DecompEval, a metric that frames NLG evaluation as an instruction-style question answering task. This approach utilizes pre-trained language models to generate evaluations, enhancing both the generalizability and interpretability of the metrics. The evaluation process can reveal the underlying reasons for scores through generated subquestions and their answers, providing greater transparency.

Furthermore, machine-learned metrics can incorporate contextual information, allowing for more accurate assessments of NLG outputs in situational contexts. This is particularly advantageous for tasks such as dialogue generation or narrative writing, where the context significantly influences the quality of the generated text. For example, "LUNA: A Framework for Language Understanding and Naturalness Assessment" presents LUNA, a framework that evaluates NLG outputs using context-aware models. By considering the broader context, these metrics offer a more holistic assessment of NLG outputs.

However, the development and application of machine-learned metrics come with several challenges. Overfitting is a major concern, where metrics may become overly specialized to the training dataset, leading to poor generalization to new data. Addressing this requires careful selection and diversification of training datasets. Additionally, the computational resources required for training and deployment can be substantial, posing practical limitations. Bias and fairness issues must also be considered, as metrics are only as unbiased as their training data. Ensuring the data is representative and diverse is crucial for mitigating these risks.

Moreover, while machine-learned metrics offer promising solutions for reference-free evaluation, they introduce complexities, particularly regarding interpretability. The internal workings of LLMs can be opaque, complicating the understanding of why certain evaluations are produced. Ongoing efforts focus on improving transparency and explainability, though significant challenges remain.

Despite these challenges, the development of machine-learned metrics marks a pivotal step forward in NLG evaluation. These metrics not only reduce dependence on human-generated references but also provide a more comprehensive evaluation framework. As the field advances, integrating advanced machine learning techniques into NLG evaluation will likely become more prevalent, enhancing the accuracy, reliability, and comprehensiveness of evaluations. Future research will continue to refine these metrics, address their limitations, and expand their applicability to a broader range of NLG tasks and domains.

### 4.4 Hybrid Approaches Combining LLMs and Human Evaluations

Hybrid approaches combining large language models (LLMs) [16; 21] with human evaluations represent a promising direction in the field of NLG evaluation, aiming to leverage the strengths of both automatic and manual assessment methods. By integrating the scalability and speed of LLMs with the nuanced judgment of human evaluators, these hybrid frameworks seek to provide more comprehensive and accurate evaluations of NLG systems. This section explores several key aspects of hybrid approaches, including their underlying mechanisms, practical implementations, and the benefits and limitations of such integrations.

Building on the advancements in machine-learned metrics, hybrid approaches offer a balanced solution to the limitations observed in both fully automated and human-only evaluation methods. Machine-learned metrics, such as those derived from LLMs, excel in rapid processing and scalability but often lack the nuanced judgment required for comprehensive evaluation. Conversely, human evaluations provide deep insights but can be time-consuming and inconsistent. Hybrid approaches address these limitations by leveraging the complementary strengths of both methods.

A common strategy in hybrid approaches involves using LLMs to preprocess NLG outputs before presenting them to human evaluators. For instance, an LLM might initially filter out obvious errors or irrelevant information, allowing human evaluators to focus on more complex and meaningful aspects of the generated text. This preprocessing step can significantly enhance the efficiency of human evaluation, enabling evaluators to allocate their time and cognitive resources more effectively. Additionally, LLMs can assist in standardizing the evaluation process by providing initial ratings or scoring guidelines that human evaluators can refine or adjust based on their subjective judgments.

One notable example of a hybrid approach is the use of LLMs to generate prompts or questions that guide human evaluators in their assessments. This technique leverages the natural language understanding capabilities of LLMs to create contextually relevant and insightful prompts, which can help human evaluators to consider various aspects of the generated text. For example, an LLM might generate a series of questions related to the coherence, relevance, and informativeness of a text summary, prompting human evaluators to provide more detailed and structured feedback. Such an approach not only facilitates a more thorough evaluation but also ensures that all critical aspects of the text are adequately assessed.

Moreover, hybrid approaches can incorporate LLMs to provide real-time feedback to human evaluators during the evaluation process. This can be achieved through interactive interfaces that allow evaluators to input their assessments and receive immediate suggestions or explanations from the LLM. For instance, if a human evaluator rates a piece of text as having low coherence, the LLM can provide reasons for this rating, potentially based on the presence of logical inconsistencies or repetitive phrases. This real-time feedback mechanism can serve as a valuable tool for human evaluators, helping them to refine their judgments and ensure consistency across different evaluations.

Another important aspect of hybrid approaches is the integration of human feedback into the training and refinement of LLMs. By incorporating human judgments into the evaluation metrics used by LLMs, these models can learn to better align their assessments with human perceptions of text quality. For example, human evaluators can rate a set of NLG outputs on various dimensions such as fluency, relevance, and coherence, and these ratings can be used to fine-tune an LLM to generate more human-like evaluations. This iterative process of human evaluation and model refinement can lead to the development of more accurate and reliable LLM-based evaluation tools.

However, despite the potential benefits of hybrid approaches, there are several challenges and limitations that need to be addressed. One significant challenge is ensuring the alignment between LLM-generated evaluations and human judgments. While LLMs can be trained to approximate human ratings, there may still be discrepancies due to differences in perception, contextual understanding, and subjective interpretation. To mitigate this issue, it is crucial to validate LLM-generated evaluations against human judgments through extensive testing and calibration. This validation process can help to identify and rectify any systematic biases or inaccuracies in LLM evaluations.

Additionally, the practical implementation of hybrid approaches requires careful consideration of the technical infrastructure and computational resources needed to support seamless interaction between LLMs and human evaluators. This includes developing user-friendly interfaces that facilitate the exchange of information and feedback between the two components. Furthermore, the scalability of hybrid approaches must be ensured to handle the large volume of data typically involved in NLG evaluations. Efficient algorithms and distributed computing solutions may be necessary to enable the simultaneous evaluation of numerous NLG outputs by both LLMs and human evaluators.

In conclusion, hybrid approaches combining LLMs with human evaluations offer a promising avenue for advancing the field of NLG evaluation. By integrating the speed and efficiency of LLMs with the nuanced judgment of human evaluators, these approaches can provide more comprehensive and accurate assessments of NLG systems. However, the successful implementation of hybrid approaches requires addressing the challenges of alignment, validation, and technical infrastructure. As research continues to advance, it is expected that hybrid evaluation frameworks will become increasingly sophisticated, leading to improved NLG system development and performance.

### 4.5 Challenges and Limitations of Reference-Free Evaluation

Reference-free evaluation techniques offer a promising avenue for assessing NLG systems without the dependency on human-generated references, thereby addressing some of the shortcomings associated with traditional metrics. However, despite their potential, these methods face several inherent challenges and limitations, which significantly impact their reliability and effectiveness. One major concern is the issue of bias, arising from the reliance on machine-generated references or evaluation models. These models, including large language models (LLMs) [15], can inherit biases present in their training data, leading to skewed evaluations, particularly if the training corpus does not adequately represent the diversity of NLG outputs or contains biases that inadvertently influence the evaluation process. This can result in outputs that do not accurately reflect the true quality of the NLG system being assessed, leading to potential inaccuracies in performance estimation.

Another limitation of reference-free evaluation techniques is their robustness under varying conditions. Many methods, including those based on LLMs, have shown susceptibility to adversarial conditions, where slight modifications to input texts can drastically alter evaluation outcomes [13]. For instance, in evaluating text summarization systems, a minor change in wording might cause a significant drop in the evaluation score, despite minimal semantic changes. This sensitivity undermines the stability and consistency of the evaluation process, making it challenging to draw reliable conclusions about the true performance of NLG systems.

Domain specificity is another critical factor impacting the efficacy of reference-free evaluation techniques. Different domains—such as healthcare, finance, and legal—require specialized understanding and expertise for accurate evaluation. Current methods often lack the domain-specific nuances necessary for informed judgments. For example, in a legal context, an LLM-based system might fail to evaluate a document accurately due to a lack of legal knowledge and vocabulary [17]. Similarly, in technical writing, where precision and accuracy are paramount, an evaluation model’s deficiency in domain-specific knowledge can lead to erroneous evaluations. This highlights the necessity for domain-specific training of evaluation models to ensure accurate assessments of NLG outputs in diverse fields.

Furthermore, the challenge of generalizability across different types of NLG tasks is significant. While some reference-free techniques excel in certain domains, they may struggle with others due to varying task complexities and requirements. For instance, open-ended generation tasks, such as dialogue systems, pose different challenges compared to structured data-to-text generation tasks [1]. Dialogue systems require an understanding of conversation flow, context, and engagement, which are less critical in structured data-to-text generation tasks. Thus, a single evaluation framework might not suffice for all tasks, necessitating task-specific evaluation methods or frameworks.

Bias in reference-free evaluation can manifest in multiple ways, affecting not only the evaluation model but also the evaluation process and data. For example, if the training data predominantly includes examples from a particular genre or style, the evaluation model may favor those outputs, disregarding other styles unfairly. Additionally, the process of generating or selecting candidate references for evaluation can introduce bias, especially if it relies on predefined templates or patterns that limit the scope of evaluation. This narrow focus fails to capture the diversity and richness of NLG outputs.

Robustness under adversarial conditions is essential for credible and reliable evaluation. The susceptibility of these methods to minor perturbations can undermine their credibility. Minor changes in input text, such as word order or synonym substitution, can significantly affect evaluation scores, indicating a lack of robustness. This sensitivity leads to inconsistent evaluations and hinders the establishment of a stable baseline for comparing different NLG systems or tracking progress over time. Moreover, the vulnerability to manipulation in adversarial scenarios, where texts are crafted to exploit evaluation metrics, further complicates the evaluation process.

Domain-specific limitations are compounded by the need for continuously updated and relevant evaluation models in rapidly evolving fields. In areas like technology and healthcare, terminology and best practices change frequently, requiring evaluation models to stay current. Continuous updating and refinement of these models can be resource-intensive and time-consuming. Additionally, the dynamic nature of these fields makes what is considered a high-quality output today potentially outdated tomorrow, complicating the evaluation process further.

Achieving consistent and fair evaluations across diverse NLG tasks necessitates a nuanced approach that acknowledges the unique demands of each task. While some tasks may benefit from standardized criteria, others require task-specific metrics. For example, dialogue generation prioritizes conversational coherence and engagement, while text summarization focuses on accuracy and informativeness. Developing a one-size-fits-all solution is challenging, highlighting the need for adaptable evaluation frameworks that cater to specific task requirements.

In conclusion, while reference-free evaluation techniques offer promising alternatives to traditional metrics, they come with their own set of challenges and limitations. Addressing these requires continuous improvement of evaluation models, incorporation of domain-specific knowledge, and development of robust frameworks capable of handling adversarial conditions and maintaining consistency across diverse NLG tasks. By tackling these challenges, the field can advance towards more reliable and comprehensive evaluation methods for NLG systems.

## 5 Taxonomy and Prospects of Large Language Model-Based Evaluations

### 5.1 Introduction to LLM-Based Evaluations

Large Language Models (LLMs) have emerged as transformative tools in the field of Natural Language Processing (NLP) and Natural Language Generation (NLG), marking a significant shift in the way we conceptualize and implement automated evaluations of NLG systems. Traditionally, the evaluation of NLG systems has relied heavily on reference-based metrics such as BLEU and ROUGE [1], which primarily focus on lexical and syntactic matches between the generated text and a set of reference texts. While these metrics serve as a foundational approach, they often fall short in capturing the deeper nuances of semantic meaning, contextual appropriateness, and creative diversity inherent in NLG outputs [3]. This limitation underscores the need for more sophisticated evaluation mechanisms capable of delivering more accurate and reliable assessments of NLG systems.

Building upon the advancements in LLMs discussed previously, these models represent a paradigm shift in NLG evaluation by leveraging advanced machine learning techniques to generate evaluations that mimic human-like assessments without the need for explicit references [7]. Trained on vast amounts of textual data, LLMs can understand and evaluate NLG outputs in a manner that transcends simple lexical matching, assessing generated texts based on their coherence, fluency, relevance, and overall quality. This approach offers a more holistic evaluation framework [9].

One of the primary advantages of LLM-based evaluations is their adaptability to the diverse and dynamic nature of NLG tasks. Unlike static reference-based metrics that may struggle to maintain consistency across varied NLG applications, LLMs can be fine-tuned or adapted to specific tasks, allowing for more contextually relevant and task-appropriate evaluations [17]. This flexibility is particularly beneficial in scenarios involving highly personalized or context-dependent content generation, such as in dialogue systems or narrative generation, where precise contextual adherence is essential.

Moreover, the reliance on LLMs for NLG evaluation streamlines and scales the assessment process, offering a significant advantage over traditional human-based evaluations [7]. Human evaluations are renowned for their high reliability and ability to capture subjective qualities often missed by automated metrics [22], yet they suffer from challenges related to consistency, scalability, and cost-efficiency. By automating the evaluation process with LLMs, researchers and developers can reduce time and resource consumption, enabling more frequent and comprehensive assessments of NLG systems [1].

However, integrating LLMs into NLG evaluations presents a series of challenges that must be addressed. Concerns around robustness and fairness, particularly in handling adversarial conditions and avoiding spurious correlations, are paramount [9]. It is crucial to prevent LLMs from favoring certain types of input or output, which could lead to biased evaluations and misrepresented system quality. Additionally, ensuring the interpretability of LLM-based evaluations is vital for building trust, as these models can produce evaluations that are difficult to understand or justify without a clear rationale [8]. Ethical and practical considerations, including privacy, bias, and potential misuse of generated content, must also be carefully managed [3].

Despite these challenges, the use of LLMs for NLG evaluations holds immense promise for advancing the field. By providing a more comprehensive and context-aware evaluation framework, LLMs can foster the development of more sophisticated and reliable NLG systems, enhancing the quality and utility of generated content across various applications. As the capabilities of LLMs continue to evolve, so too will the potential for refining and broadening the scope of LLM-based evaluations, driving more nuanced and accurate assessments of NLG systems.

### 5.2 Core Concepts and Development Trends

Large language models (LLMs) [10] represent a significant advancement in the field of Natural Language Generation (NLG) and are at the forefront of recent developments in NLG evaluations. These models are characterized by their extensive scale, complexity, and capacity to handle a wide array of linguistic tasks and nuances. At the heart of LLMs lies a sophisticated architecture that enables them to process and generate text with remarkable coherence and fluency, thereby facilitating the development of more accurate and reliable NLG systems. The architecture of LLMs typically comprises a transformer-based framework, featuring self-attention mechanisms that allow the model to weigh the importance of different words within a sentence, thereby capturing contextual dependencies. This architectural design supports the model’s ability to generate text that is not only grammatically correct but also semantically rich and contextually appropriate.

Training LLMs involves a complex process that leverages vast amounts of textual data to enable the model to learn and generalize over a wide range of linguistic phenomena. During training, the LLM is exposed to diverse sources of data, including books, articles, web pages, and interactive dialogues. These data sources are preprocessed and tokenized to ensure efficient learning, where each token is represented numerically. The model is then optimized using gradient descent algorithms, which adjust its parameters iteratively to minimize the difference between predicted and actual outcomes. This iterative refinement enhances the model's predictive accuracy and deepens its understanding of language.

Recent advancements in training LLMs include innovations such as masked language modeling (MLM) and unsupervised learning. MLM involves masking certain tokens in a sentence and training the model to predict these tokens based on the surrounding context. This technique fosters a deeper understanding of syntactic and semantic relationships, enhancing the model’s robustness against adversarial attacks [6]. Unsupervised learning, another key innovation, enables the model to learn from raw text without explicit labels, promoting the discovery of latent patterns and structures that improve the coherence and relevance of generated text.

Interpretability has become a focal point in LLM research, given the need to understand and validate the model’s decision-making processes. Tools like attention visualization and layer-wise relevance propagation offer insights into how LLMs process and generate text. Attention visualization highlights the model’s focus on specific words or phrases, revealing its reasoning process. Layer-wise relevance propagation breaks down the model’s predictions into contributions from individual features, aiding in the identification of potential biases or inaccuracies.

Multilingual and cross-cultural capabilities are also advancing, reflecting a move away from monolingual NLG systems towards models that can operate effectively in diverse linguistic environments. Multilingual LLMs are trained on a variety of linguistic resources across different languages, generating culturally appropriate and linguistically accurate text. These models adapt to regional dialects and variations, enhancing their applicability in a global context.

By integrating LLMs into NLG evaluations, evaluators can generate more nuanced and context-aware assessments of NLG outputs. For example, LLMs can evaluate the coherence and relevance of generated text, complementing traditional metrics like BLEU and ROUGE that may overlook these attributes. Additionally, LLMs can identify and mitigate hallucinations, thus enhancing the reliability and trustworthiness of NLG systems.

However, several challenges persist, including the computational demands of training and deploying LLMs, the difficulty in diagnosing issues due to their black-box nature, and the risk of biases and inaccuracies. Addressing these challenges is essential for fully leveraging the potential of LLMs in NLG evaluations.

### 5.3 Methods and Techniques for LLM-Based Evaluations

Large Language Models (LLMs) have emerged as powerful tools for evaluating the quality of Natural Language Generation (NLG) outputs, offering both automated metrics and manual assessment methods that contribute significantly to the comprehensive evaluation of these systems. These methods leverage the computational capabilities of LLMs to provide quick and scalable evaluations while also incorporating human insights to capture nuanced aspects of NLG output.

Automated metrics derived from LLMs are designed to utilize the model’s internal representations and predictive capabilities to generate evaluations. One common approach involves fine-tuning LLMs on specific evaluation tasks to produce scores that reflect the quality of NLG outputs. For instance, LLMs can generate evaluations without requiring human references, alleviating the burden of manual annotation [17]. This method involves feeding the LLM with the generated text and obtaining a score or qualitative assessment based on the model’s learned representations, which are often aligned with human judgments, making them valuable for gauging the quality of NLG systems across various domains.

Additionally, LLMs can perform perturbation checks on NLG outputs, a method introduced in the study 'Perturbation CheckLists for Evaluating NLG Evaluation Metrics'. This technique involves generating perturbed versions of the input text and observing how LLMs react to these changes. Perturbation checks help uncover the sensitivity of LLM-based metrics to specific attributes of the NLG output, such as coherence, relevance, and fluency. By systematically varying these attributes, researchers can gain insights into the robustness and limitations of LLM-based evaluations, ensuring that these metrics are reliable across different scenarios.

Another significant application of LLMs in NLG evaluation is their use in formulating evaluation metrics that require minimal or no training on specific datasets. For example, DecompEval uses pre-trained LLMs to formulate NLG evaluation as an instruction-style question answering task [8]. This approach not only enhances the generalization ability of the evaluation metric but also ensures interpretability by decomposing the evaluation into subquestions. Each subquestion is evaluated separately, providing detailed feedback on specific aspects of the NLG output. This method is advantageous because it avoids overfitting to task-specific datasets, making it a versatile tool for evaluating a wide range of NLG systems.

Manual assessments, involving human evaluators, are often complemented by LLM-generated feedback to capture nuances that automated metrics might miss. Human evaluators rate the quality of NLG outputs based on criteria such as clarity, informativeness, and engagement, while LLMs provide additional insights through automated analysis. This hybrid approach combines the strengths of human judgment and machine efficiency, leading to more nuanced and accurate evaluations.

Notably, frameworks like control variates integrate human assessments with LLM-generated evaluations to mitigate biases and ensure robust estimations of NLG quality. Control variates are statistical techniques used to reduce variance in outcome estimation, a concept explored in 'Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory'. In NLG evaluation, control variates can help balance individual human evaluator biases and the limitations of LLMs, leading to more reliable evaluations.

Moreover, LLMs can generate synthetic texts for benchmarking purposes, simulating various scenarios to assess NLG systems across a wide spectrum of tasks and domains. The GEM Benchmark, for example, introduces a suite of tasks and datasets designed to evaluate NLG systems in a standardized manner [4]. This benchmark facilitates comparisons among different NLG systems and encourages the development of more robust and versatile metrics.

In summary, the combination of automated and manual methods forms a comprehensive evaluation framework that addresses the multifaceted nature of NLG outputs. Automated metrics provide rapid and scalable evaluations, while manual assessments ensure evaluations are grounded in human perception and context. Together, these methods enhance the reliability and validity of NLG evaluations, fostering advancements in the field. However, the performance of LLMs can be influenced by the quality and diversity of the training data, leading to potential biases. Ongoing research is necessary to refine and enhance LLM-based evaluation methods, ensuring their continued effectiveness and adaptability.

### 5.4 Case Studies and Benchmarks

Case studies and benchmarks provide a critical perspective on the practical application of large language models (LLMs) in the evaluation of NLG systems. These studies offer valuable insights into the real-world efficacy and limitations of LLMs, illustrating both their successes and challenges. By exploring a series of case studies and benchmark evaluations, we delve into the nuances of LLM-based evaluations and highlight key learnings from these applications.

One notable application of LLMs in NLG evaluation is the assessment of factual consistency, where LLMs serve as powerful tools for detecting inaccuracies and inconsistencies in generated texts. The study 'AlignScore: Evaluating Factual Consistency with a Unified Alignment Function' introduces AlignScore, a metric that evaluates the factual consistency of generated texts based on a unified alignment function. This function leverages a large diversity of data sources to align information between two arbitrary text pieces, demonstrating substantial improvements over existing metrics across various benchmarks, including 22 evaluation datasets. Despite these successes, reliance on pre-existing datasets raises concerns about generalizability, indicating a need for further exploration in creating adaptable models capable of handling unforeseen scenarios [16].

Another significant application involves the detection of factual errors through cross-examination. The study 'LM vs LM: Detecting Factual Errors via Cross Examination' proposes a method where one LLM questions the factual claims of another, revealing inconsistencies and enabling the detection of errors. This approach is highly effective, often outperforming existing methods, though it heavily depends on the quality and comprehensiveness of the LLM’s knowledge base. Continuous updates to training datasets are essential to ensure the accuracy and reliability of such evaluations [21].

Human-machine collaboration further enhances NLG evaluations. LLMs, when used alongside human judgments, facilitate more efficient and comprehensive assessments. The study 'Perturbation CheckLists for Evaluating NLG Evaluation Metrics' explores perturbation checklists to target specific criteria by introducing controlled perturbations to generated texts. Findings reveal that existing metrics often fail to detect these perturbations accurately, highlighting the complexity in capturing nuanced aspects like fluency and coherence. Integrating human insights complements automated evaluations, emphasizing the importance of hybrid frameworks that leverage both human expertise and LLM capabilities [5].

LLMs have also advanced evaluations by simulating human-like interactions. The study 'Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications' investigates the challenges and opportunities in scientific applications, employing LLMs to simulate human responses for evaluating accuracy and coherence. This approach aids in detecting logical and semantic errors and provides a framework for continuous improvement through iterative evaluation and refinement. However, the reliance on LLMs for simulation raises concerns about capturing the full spectrum of human cognition and judgment, suggesting ongoing research is needed to enhance cognitive fidelity [23].

A unique approach is taken in 'Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models,' which models factual queries as constraint satisfaction problems. By examining attention patterns during generation, the study uncovers a strong positive correlation between attention to constraint tokens and factual accuracy, offering a novel perspective on text generation. However, current LLM architectures struggle to fully satisfy complex constraints, indicating a need for further advancements in model design and training [24].

Lastly, LLMs have been applied in specific tasks and datasets to evaluate factual consistency. The study 'TRUE: Re-evaluating Factual Consistency Evaluation' evaluates various metrics across standardized texts from diverse tasks, finding strong performance from large-scale NLI and question generation-and-answering approaches. Yet, reliance on specific datasets and tasks raises questions about generalizability, underscoring the need for more comprehensive evaluation frameworks [25].

In conclusion, the practical application of LLMs in NLG evaluations offers both opportunities and challenges. While LLMs enhance the accuracy and reliability of evaluations, they also highlight the ongoing need for innovation and refinement in methodologies. Through case studies and benchmarks, we gain a clearer understanding of LLMs’ strengths and limitations, paving the way for future advancements in NLG evaluation.

### 5.5 Strengths and Limitations of LLM-Based Evaluations

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LLMs offer several notable advantages in the evaluation of NLG systems, particularly in terms of their ability to generate evaluations without the need for explicit human references, thus potentially streamlining the evaluation process. Notably, the efficiency of LLM-based evaluations is a primary strength. By leveraging pre-trained models, evaluators can automate the generation of assessments, significantly reducing the time and resources required for evaluations [15]. This automation not only accelerates the evaluation process but also enhances scalability, enabling the assessment of larger datasets and an increased number of generated texts.

Additionally, LLMs can simulate a wide array of evaluation criteria, from basic grammatical correctness to more nuanced aspects like coherence and relevance, thereby providing a more comprehensive evaluation framework. For instance, MARS [26] has shown superior performance in aligning with human judgments by considering the context of the generated text, demonstrating the contextual awareness of LLMs and their capability to offer more accurate and relevant feedback, especially for open-ended tasks where context plays a crucial role in determining the quality and appropriateness of the generated text.

However, despite their significant potential, LLM-based evaluations face inherent limitations and challenges. Bias is a prominent issue; recent studies have highlighted that LLMs may inherit biases present in their training data, leading to biased evaluations [13]. For example, if training data contains gender or racial biases, these biases can be reflected in LLM evaluations, potentially resulting in unfair assessments of NLG systems. Mitigating such biases requires careful data selection and implementation of strategies to reduce bias during the training phase.

Reliability is another concern, as LLM evaluations may not always correlate with human perceptions of quality. As noted in "Evaluation Discrepancy Discovery - A Sentence Compression Case-study," high metric scores do not necessarily indicate human-perceived quality, suggesting that LLMs might sometimes overfit to certain data patterns rather than genuinely reflecting text quality. This discrepancy emphasizes the need for a multifaceted evaluation approach that combines the strengths of LLMs with human oversight and validation.

Moreover, the performance of LLM-based evaluations hinges on the quality and diversity of the input data. If the training data lacks diversity or fails to represent real-world scenarios, the evaluations may not accurately reflect the true performance of NLG systems. Continuous data curation and updating, alongside ongoing refinement of LLMs, are essential to maintain the relevance and accuracy of evaluations.

Interpretability is also a challenge. Unlike traditional metrics that provide straightforward numerical scores, LLM outputs can be complex and less immediately understandable. This complexity complicates the task of understanding evaluation outcomes and identifying areas for NLG system improvement. Enhancing transparency and interpretability is crucial for supporting iterative development and refinement of NLG systems.

Despite these challenges, LLMs continue to show great promise for NLG evaluation. Advancements in LLM architectures, training techniques, and access to diverse, high-quality training data are expected to improve the performance and reliability of LLM-based evaluations. For example, integrating multimodal data and developing sophisticated attention mechanisms could enable LLMs to better understand and evaluate the nuances of generated texts. Ongoing research into mitigating biases and enhancing interpretability also holds potential solutions to current limitations.

In summary, while LLMs bring increased efficiency, contextual awareness, and the ability to evaluate multiple criteria simultaneously, they must overcome challenges related to bias, data quality, and interpretability to fully realize their potential in NLG evaluation. Addressing these challenges through continuous improvement and innovation is vital for advancing the field of NLG.
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### 5.6 Future Directions and Emerging Trends

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Future developments in the utilization of large language models (LLMs) for Natural Language Generation (NLG) evaluations promise significant advancements, encompassing improvements in model performance, enhanced integration with human judgments, and the advent of innovative evaluation paradigms. The continued evolution of LLMs, driven by ongoing research into their underlying architectures, training methodologies, and the diversity and scale of the datasets they are trained on, is anticipated to make them even more adept at understanding and generating nuanced, contextually rich text. This evolution will facilitate more accurate and insightful evaluations of NLG systems.

However, the reliance on LLMs for evaluation raises concerns about reliability, particularly due to the inherent limitations of the data they were trained on and the potential for bias. Future research should focus on mitigating these issues by fine-tuning LLMs specifically for evaluation purposes and incorporating domain-specific knowledge to enhance their adaptability and precision. Additionally, robust validation methods are needed to ensure the consistency and fairness of LLM-based evaluations across different tasks and contexts.

A key area of focus will be the integration of human judgment with LLM evaluations, creating hybrid frameworks that combine the strengths of both approaches. While LLMs excel at providing fast, scalable evaluations, human evaluators remain indispensable for capturing subtle nuances and subjective qualities essential for comprehensive assessment. The use of control variates in human-AI collaboration offers a promising approach to mitigate biases and improve the reliability of evaluations. Future studies could build upon this foundation, investigating advanced techniques for aligning human and machine assessments, such as leveraging consensus methods or integrating human feedback into the iterative refinement of LLM-based evaluations.

Another frontier in NLG evaluation is the exploration of novel evaluation paradigms that leverage the evolving capabilities of LLMs. One such paradigm is multi-aspect evaluation, where the performance of NLG systems is assessed across multiple dimensions simultaneously. This approach aims to capture a more holistic view of the generated text, reflecting its various facets such as fluency, coherence, factuality, and relevance. As LLMs continue to evolve, they may offer increasingly sophisticated tools for multi-aspect evaluation, leading to more nuanced and context-aware assessments. For example, using LLMs to formulate and answer a series of detailed questions about the generated text can provide a comprehensive evaluation that captures deeper semantic and pragmatic aspects of the text.

The integration of large-scale datasets and advanced data augmentation techniques can further enhance the capabilities of LLMs in NLG evaluation. Exposing LLMs to a broader range of data, including diverse languages, dialects, and cultural contexts, fosters the development of more inclusive and adaptable evaluation frameworks. This addresses the challenge of evaluating NLG systems in specialized domains and promotes the creation of evaluation metrics that are culturally sensitive and linguistically diverse. Additionally, synthetic data generation techniques can provide a scalable solution for augmenting real-world datasets, enabling the training of LLMs on a wide variety of scenarios and thereby improving their generalization capabilities.

Looking ahead, the research community should focus on developing transparent and interpretable evaluation frameworks to ensure that the inner workings of LLMs used for evaluation are comprehensible and justifiable. As the complexity of LLMs grows, efforts to enhance transparency could include the development of visualization tools that allow stakeholders to understand the decision-making process of LLMs, as well as the establishment of guidelines for reporting and interpreting evaluation results. These efforts will foster greater trust in LLM-based evaluations and facilitate wider adoption among practitioners and researchers.

In conclusion, the future of NLG evaluation is poised to witness transformative changes driven by advances in LLM technology. Realizing this potential requires sustained efforts to address current limitations and innovate in areas such as human-machine collaboration, multi-aspect evaluation, and interpretability. By embracing these challenges, the field of NLG evaluation can continue to evolve, offering ever more precise and meaningful insights into the performance of NLG systems.
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## 6 Multi-Aspect Evaluation Paradigms

### 6.1 Motivation for Multi-Aspect Evaluation

The transition to multi-aspect evaluation paradigms in Natural Language Generation (NLG) represents a significant evolution in the methodology used to assess the performance of NLG systems. This shift is driven by the recognition that NLG outputs are inherently multifaceted, encompassing various dimensions such as fluency, informativeness, coherence, relevance, and creativity [1]. Each of these dimensions contributes uniquely to the overall quality of the generated text, underscoring the need for evaluation methods that can capture the nuances of these diverse aspects.

Traditional evaluation metrics, such as reference-based metrics like BLEU and ROUGE, have long been the cornerstone of NLG evaluation. These metrics primarily focus on surface-level similarities between generated text and reference texts, such as n-gram overlap and precision/recall scores. While these metrics offer some insight into the syntactic and lexical accuracy of NLG outputs, they often fall short in reflecting the true quality of the generated text, especially in contexts where NLG systems are tasked with generating highly creative and contextually rich content [1]. This limitation is exacerbated by the increasing sophistication of modern NLG systems, which produce outputs that require a more comprehensive assessment beyond mere lexical similarity.

The realization that no single metric can fully capture the complexity of NLG outputs is a key driver for developing multi-aspect evaluation paradigms. Recent studies have shown that traditional metrics, despite their widespread adoption, only weakly correlate with human judgments in scenarios involving highly creative and contextually rich content [1]. This disconnection highlights the necessity for evaluation metrics that provide a more holistic assessment of NLG performance, taking into account the multitude of factors that contribute to the quality of generated text.

The advent of large language models (LLMs) has further underscored the need for multi-aspect evaluation. LLMs, with their vast knowledge bases and generative capabilities, present unique challenges and opportunities for NLG assessment. Traditional metrics, initially developed for simpler models, often fail to accurately evaluate the output of LLMs, which can generate highly varied and contextually complex text. This limitation becomes particularly problematic when evaluating LLM-generated content, as single-aspect metrics fail to capture the full spectrum of output variability [7].

Moreover, the complexity of NLG tasks themselves drives the need for multi-aspect evaluation. Tasks such as dialogue generation and image captioning require high levels of linguistic proficiency, context understanding, and emotional sensitivity. These factors introduce additional layers of complexity that cannot be adequately captured by metrics focused solely on surface-level features. Multi-aspect evaluation paradigms are designed to consider these various dimensions, ensuring a more accurate and nuanced assessment of NLG system performance [22].

The integration of human judgment into NLG evaluation further emphasizes the need for multi-aspect approaches. Human evaluators excel at recognizing the subtle nuances and qualitative attributes of NLG outputs, making their input invaluable. However, the subjectivity inherent in human judgment necessitates a balanced approach between human assessment and automated metrics. Multi-aspect evaluation paradigms can bridge this gap by incorporating human feedback into a broader evaluation framework that considers multiple dimensions of NLG performance. This hybrid approach leverages the strengths of both human and automated assessments, ensuring a more robust and reliable evaluation process [1].

Ongoing advancements in NLG research continually drive the need for more sophisticated evaluation methods. As NLG systems advance, the focus shifts toward assessing not just the quality of the text but also its utility, relevance, and adherence to ethical standards. Multi-aspect evaluation paradigms are well-suited to meet these evolving demands, offering a flexible and adaptable framework that accommodates the changing landscape of NLG research [3].

In conclusion, the motivation for transitioning to multi-aspect evaluation paradigms in NLG stems from addressing the multifaceted nature of NLG outputs, overcoming the limitations of traditional single-aspect metrics, and accommodating the increasing complexity of NLG tasks and models. By adopting a more comprehensive and nuanced approach to NLG evaluation, researchers and practitioners can gain a deeper understanding of the strengths and weaknesses of NLG systems, ultimately leading to more effective and reliable NLG solutions.

### 6.2 CoAScore - A Case Study

CoAScore is a multi-aspect evaluation framework designed to provide a comprehensive and nuanced assessment of natural language generation (NLG) outputs. Building upon the recognition that NLG outputs are multifaceted, CoAScore leverages the inter-correlation of various aspects through a Chain-of-Aspects prompting technique, offering a holistic view that surpasses the limitations of traditional single-aspect evaluation metrics. By incorporating multiple evaluative criteria such as coherence, fluency, relevance, and informativeness, CoAScore enhances the reliability and accuracy of NLG system evaluations.

The foundation of CoAScore lies in its approach to evaluating NLG outputs by considering multiple facets of the generated text. Unlike traditional metrics that often focus on surface-level features such as n-gram overlaps, CoAScore delves deeper into the structural and semantic elements of the text. This multi-faceted evaluation is achieved through a series of carefully designed prompts that guide the evaluation process, ensuring that each aspect is thoroughly assessed. The inter-correlation of these aspects is critical, as it allows for a more accurate reflection of the overall quality of the generated text. For instance, the fluency of a piece of text is inherently linked to its coherence; a text that flows smoothly is likely to be more coherent than one that is disjointed and confusing.

Understanding how CoAScore operates requires an exploration of its core components: the aspects, the prompting technique, and the scoring mechanism. The aspects considered in CoAScore include, but are not limited to, coherence, informativeness, relevance, fluency, and consistency. Each aspect is defined based on specific criteria relevant to the NLG task at hand. For example, coherence might be evaluated based on how logically connected the sentences are within a paragraph, while informativeness could be assessed by the extent to which the generated text provides meaningful and valuable information to the reader.

A key innovation of CoAScore is the Chain-of-Aspects prompting technique. This technique involves sequentially prompting evaluators to consider each aspect of the generated text in a structured manner. For instance, the evaluation process might start with a prompt that asks evaluators to assess the coherence of the text, followed by a prompt to evaluate the informativeness, and so forth. This sequential evaluation ensures that each aspect is thoroughly examined, and the inter-correlation between aspects is taken into account. After evaluating the coherence of a piece of text, evaluators are prompted to consider how the coherence relates to the informativeness and relevance of the text. This chained evaluation helps to avoid the pitfall of treating each aspect in isolation, a common issue with traditional evaluation metrics.

The scoring mechanism in CoAScore aggregates the evaluations of multiple aspects into a single, comprehensive score. This score reflects the overall quality of the generated text by considering the interplay between different aspects. For example, a highly coherent and informative text that lacks relevance might receive a lower score compared to a text that balances coherence, informativeness, and relevance. The aggregation of scores is performed using a weighted sum approach, where each aspect is assigned a weight based on its relative importance to the NLG task. These weights are determined through extensive experimentation and validation to ensure that the final score accurately reflects the desired quality of the generated text.

One of the significant advantages of CoAScore is its flexibility in accommodating different NLG tasks. The framework allows for customization of aspects and prompts based on the specific requirements of each task. For example, in the context of dialogue generation, aspects such as engagement and context awareness might be prioritized over informativeness. Similarly, in the realm of text summarization, the prominence of informativeness and conciseness might be emphasized. This flexibility enables CoAScore to be adapted to various NLG tasks, making it a versatile tool for NLG evaluation.

Empirical validation through a series of experiments has demonstrated CoAScore's effectiveness in providing more accurate and reliable evaluations compared to traditional single-aspect metrics. For instance, in a study comparing CoAScore to metrics like BLEU and ROUGE, it was found that CoAScore achieved higher correlations with human judgments across a variety of NLG tasks [27]. This indicates that CoAScore's multi-aspect approach captures a wider range of evaluative criteria, resulting in a more comprehensive assessment of NLG quality.

However, CoAScore is not without its challenges. The complexity of the evaluation process, involving multiple evaluations per generated text and the need to consider the inter-correlation of aspects, can be time-consuming and resource-intensive. Customizing aspects and prompts for different tasks can also be labor-intensive, requiring domain expertise and careful consideration of task-specific requirements. Another limitation is its reliance on human evaluators to some extent, even though the framework can be partially automated using machine learning techniques. This reliance can introduce subjectivity, varying based on individual evaluator interpretation. To mitigate this, CoAScore employs rigorous training and calibration processes for evaluators to ensure consistency and reliability in the evaluation outcomes.

Despite these challenges, CoAScore represents a significant advancement in NLG evaluation, offering a more nuanced and accurate reflection of NLG quality. As the field continues to evolve, frameworks like CoAScore are expected to play an increasingly important role in driving advancements in model development and ensuring that NLG systems meet evolving user needs. Future research could focus on further automation of the evaluation process, enhancing adaptability to different tasks, and improving the robustness and generalizability of the scoring mechanism, essential steps toward realizing CoAScore's full potential as a leading tool for NLG evaluation.

### 6.3 Comparative Analysis of Multi-Aspect Evaluation Methods

Conducting a comparative analysis of different multi-aspect evaluation methods is essential for understanding their strengths and limitations, ultimately guiding the selection and development of more effective evaluation paradigms in the realm of Natural Language Generation (NLG). Building upon the theoretical and empirical foundations of frameworks like CoAScore, this subsection explores several methodologies that address multiple facets of NLG outputs, ranging from grammatical correctness to thematic relevance and beyond.

One notable methodology discussed in 'Why We Need New Evaluation Metrics for NLG' emphasizes the necessity for novel evaluation metrics that can provide a more nuanced understanding of system performance. Although this paper does not explicitly mention CoAScore, it underscores the importance of multi-aspect evaluation methods. These methods break down the assessment into multiple interconnected dimensions, such as fluency, coherence, informativeness, and accuracy, ensuring that the evaluation reflects a holistic view of the generated text. This approach is particularly beneficial for tasks where different attributes interact significantly, such as dialogue generation or narrative creation.

Another method worth examining is DecompEval, which leverages instruction-tuned pre-trained language models to evaluate NLG outputs without the need for specific training on evaluation datasets [8]. The key advantage of DecompEval lies in its ability to provide dimension-level interpretability and strong generalization across various NLG tasks. By formulating the evaluation as an instruction-style question answering task and decomposing complex evaluation instructions into simpler sub-questions, DecompEval allows for a more granular analysis of generated texts. For instance, it can reveal whether a summary is informative but less coherent, providing clear insights into the specific aspects contributing to these scores. This level of detail is invaluable for debugging NLG systems and improving their performance iteratively.

In contrast, methods that rely on large language models (LLMs) to evaluate NLG outputs have gained traction, as highlighted in 'Leveraging Large Language Models for NLG Evaluation - A Survey'. The GEM benchmark, for example, provides a platform for evaluating NLG models across multiple tasks, integrating both automatic metrics and human evaluations [4]. This comprehensive approach allows researchers to gauge the performance of NLG models across a spectrum of tasks and dimensions, facilitating a more balanced assessment. However, the reliance on human evaluations can be time-consuming and resource-intensive, limiting the scalability of the GEM framework in some cases.

Additionally, methods emphasizing automated metrics, such as those proposed in 'Perturbation CheckLists for Evaluating NLG Evaluation Metrics', highlight the importance of designing evaluation metrics that are robust against perturbations [5]. These checklists aim to expose the limitations of existing metrics by systematically testing their sensitivity to specific criteria changes. For example, a checklist might assess how a metric responds when the coverage of generated text is deliberately reduced while keeping other aspects constant. Such targeted perturbations are crucial for understanding the nuances of NLG evaluation metrics and improving their reliability. However, these methods require careful design and may not fully capture the complexity of human judgments, potentially oversimplifying the evaluation process.

The advent of LLMs has introduced new possibilities for NLG evaluation. As explored in 'Leveraging Large Language Models for NLG Evaluation - A Survey', LLMs can be utilized to generate evaluations that mimic human judgments, providing a scalable and efficient alternative to traditional reference-based metrics. These models can simulate the cognitive processes involved in human evaluation, offering a more intuitive way to assess NLG outputs. However, the effectiveness of LLM-based evaluations depends heavily on the quality and diversity of the training data, as well as the alignment between the model's learned representations and the intended evaluation criteria. Issues related to bias and contextual sensitivity remain significant challenges that need to be addressed to ensure the reliability of LLM-based evaluations.

Despite the promise of these multi-aspect evaluation methods, they come with their own set of limitations. For instance, the reliance on fine-grained annotations for each aspect can be labor-intensive to obtain. Similarly, DecompEval’s effectiveness hinges on the quality of instruction-tuned models and the interpretability of their responses, which may vary across different tasks and languages. Perturbation checklists offer valuable insights but may not fully replicate the complexity of human evaluative processes. Lastly, while LLMs can provide a scalable solution, they require careful calibration and validation to avoid reinforcing biases present in their training data.

In conclusion, the comparative analysis of multi-aspect evaluation methods reveals a landscape rich with potential yet fraught with challenges. Each method brings unique advantages and faces distinct limitations, underscoring the need for a flexible and adaptable evaluation framework that can accommodate the diverse requirements of different NLG tasks and contexts. Future work should focus on integrating the strengths of these methods, while addressing their respective drawbacks, to develop more robust and comprehensive evaluation paradigms for NLG systems.

### 6.4 Practical Implementation and Applications

The practical implementation of multi-aspect evaluation paradigms, such as CoAScore, offers numerous operational benefits for Natural Language Generation (NLG) systems. By leveraging the inter-correlation of different aspects, these frameworks enable a more nuanced and comprehensive assessment of NLG outputs, thereby providing a clearer picture of their overall quality and utility. This section explores the implementation details and real-world applications of such paradigms, highlighting their operational benefits and the insights they provide into the effectiveness and reliability of NLG systems.

One of the primary operational benefits of multi-aspect evaluation is its ability to provide a balanced assessment of NLG outputs across various dimensions. Unlike traditional reference-based metrics, such as BLEU and ROUGE, which often focus narrowly on surface-level features like n-gram overlap, multi-aspect metrics consider a wider array of criteria, including fluency, coherence, relevance, and accuracy. This broadened scope allows for a more holistic evaluation of NLG systems, enabling developers and researchers to pinpoint specific strengths and weaknesses that might otherwise go unnoticed. For example, a NLG system might excel in generating coherent narratives but falter in maintaining factual accuracy. A multi-aspect evaluation framework would capture both aspects, providing a more accurate representation of the system’s performance.

Moreover, multi-aspect evaluation paradigms are particularly beneficial in complex tasks that require the integration of multiple cognitive skills. Consider the task of generating summaries from lengthy documents, which demands the ability to extract salient information and maintain the document’s overall narrative structure and logical flow. CoAScore, through its Chain-of-Aspects prompting technique, evaluates these varied requirements sequentially, capturing the system’s capability to manage multiple aspects simultaneously. This precision enhances the evaluation of NLG systems in intricate scenarios, where traditional metrics may fall short.

Real-world applications of multi-aspect evaluation paradigms are increasingly evident in industries reliant on NLG systems. In customer service, where dialogue systems interact with customers to address inquiries and complaints, the quality of generated responses directly impacts customer satisfaction and brand reputation. Multi-aspect evaluation frameworks can assess these responses for fluency, relevance, and their ability to resolve issues effectively and empathetically. This multifaceted approach provides crucial insights into the strengths and limitations of dialogue systems, guiding targeted improvements.

Similarly, personalized content generation, such as tailored news articles, relies on NLG systems that balance informativeness with readability and personal engagement. A multi-aspect evaluation framework evaluates not only factual accuracy and depth but also stylistic appeal and personal resonance. This comprehensive assessment aids content creators in optimizing NLG systems for enhanced user satisfaction.

Furthermore, the implementation of multi-aspect evaluation paradigms fosters innovation and drives the development of more advanced NLG systems. By exposing the nuances of NLG performance, these frameworks motivate researchers and practitioners to address identified gaps. For example, if a multi-aspect evaluation identifies a weakness in generating coherent long-form texts, developers might focus on improving narrative structuring. If the evaluation highlights deficiencies in maintaining factual accuracy, efforts could target enhancing knowledge representation and reasoning.

Successful implementation of multi-aspect evaluation paradigms requires careful design and calibration. Each evaluated aspect must be clearly defined and appropriately weighted for a fair and meaningful assessment. For instance, in image captioning, visual accuracy and textual fluency must be balanced to comprehensively evaluate the system’s performance. Adaptability to task-specific characteristics ensures tailored assessments maximizing utility.

The adoption of multi-aspect evaluation paradigms has been facilitated by large language models (LLMs). LLMs, with extensive knowledge bases and natural language understanding, offer powerful tools for multi-faceted evaluations aligned with human judgments. However, ensuring robustness against adversarial conditions and maintaining fairness and transparency in the evaluation process remains crucial. Addressing these challenges requires ongoing research and collaboration.

In conclusion, the practical implementation of multi-aspect evaluation paradigms enhances the effectiveness and reliability of NLG systems through a more comprehensive and nuanced assessment. As the field evolves, these paradigms are expected to drive innovation, improving the quality of NLG systems across applications. Insights gained from such evaluations will shape future NLG research and development, advancing more sophisticated and user-friendly technologies.

### 6.5 Limitations and Future Work

Despite the promising strides made in multi-aspect evaluation paradigms, several limitations persist, necessitating further exploration and refinement. One major limitation lies in the complexity and heterogeneity of human judgment, which is inherently subjective and varies across individuals. Existing metrics, including those derived from multi-aspect frameworks like CoAScore, often struggle to fully capture the nuances of human evaluation, leading to discrepancies between automatic and human assessments, as emphasized by [1].

Another critical limitation is the adaptability and generalizability of these metrics across diverse NLG tasks and domains. While CoAScore demonstrates effectiveness in certain tasks, it may not perform equally well in others due to the varying demands and characteristics of different NLG tasks, such as summarization, dialogue generation, and question generation [14].

Moreover, the reliance on human-labeled references poses another challenge, particularly in tasks where high-quality references are difficult to obtain or where the output space is highly varied, such as in creative writing or dialogue systems [17]. This issue is compounded by the fact that even with multiple references, as suggested by [14], the problem of data leakage and limited reference diversity remains a concern, potentially undermining the reliability of evaluation metrics.

The integration of machine learning techniques, especially large language models (LLMs), in multi-aspect evaluation introduces its own set of challenges. LLMs, while powerful, may introduce biases and inconsistencies in evaluation due to their inherent limitations in understanding and interpreting complex linguistic structures and contextual cues [13]. Additionally, the reliance on LLMs alone can lead to oversimplified evaluations that miss the subtleties and complexities that human evaluators can discern, as highlighted by [15].

Another significant limitation is the computational cost and resource requirements associated with multi-aspect evaluations. These evaluations often require substantial computational resources, making them less accessible to researchers and developers with limited resources. Furthermore, the process of generating multiple aspects for evaluation can be time-consuming and labor-intensive, posing practical challenges in the widespread adoption of these methods [28].

Given these limitations, several avenues for future research and improvement emerge. First, there is a need to develop more sophisticated methods for integrating human judgments with automatic evaluations to enhance the comprehensiveness and reliability of multi-aspect evaluations. This could include the use of crowd-sourced evaluation methods, advanced statistical techniques for aggregating human judgments, and the development of hybrid evaluation frameworks that leverage both human and machine evaluations [5]. Such hybrid approaches could help mitigate the subjectivity and variability inherent in human evaluations while leveraging the strengths of automatic metrics.

Second, researchers should address the issue of data leakage and the reliance on high-quality references. Innovative solutions, such as the use of synthetic data generation techniques, could create a diverse set of references that better represent the variability and complexity of real-world NLG tasks [18]. Additionally, developing unsupervised or semi-supervised methods for generating references could alleviate the burden of manual reference creation and increase the accessibility of multi-aspect evaluations.

Third, future work should focus on improving the interpretability and transparency of multi-aspect evaluation metrics. This involves enhancing the clarity of the evaluation criteria and ensuring that the metrics provide meaningful insights into the strengths and weaknesses of NLG systems. Developing more intuitive and user-friendly interfaces for presenting evaluation results could aid in fostering broader acceptance and adoption of these metrics.

Fourth, the robustness and fairness of multi-aspect evaluation metrics should be prioritized. This includes addressing the biases and limitations of LLMs in evaluation and developing strategies to ensure consistency across different demographic groups and cultural contexts. Research into adversarial evaluation techniques, such as the use of perturbation attacks, could provide valuable insights into improving the reliability and fairness of multi-aspect evaluations [13].

Finally, ongoing efforts should aim to reduce the computational cost and resource requirements associated with multi-aspect evaluations. Optimizing algorithms and computational processes, leveraging cloud computing and distributed processing technologies, and exploring cost-effective evaluation strategies could balance accuracy with resource efficiency. By addressing these limitations and exploring these avenues for future research, the field of multi-aspect evaluation paradigms stands to make significant advancements in the comprehensive and reliable evaluation of NLG systems.

## 7 Task-Specific Evaluation Metrics

### 7.1 Communication-Based Evaluation for NLG

Communication-based evaluation metrics specifically designed for NLG systems play a pivotal role in assessing the communicative effectiveness and relevance of generated texts in various contexts. Unlike traditional metrics like BLEU and ROUGE, which primarily focus on surface-level features such as n-gram overlap, communication-based metrics aim to evaluate the quality of communication, a core aspect of NLG tasks. These metrics are essential because they go beyond simple lexical overlap, capturing whether the generated text accurately and appropriately conveys its intended message to the target audience.

A prominent example of a communication-based evaluation metric is the use of dialogue systems to assess the effectiveness of generated responses. Dialogue systems require NLG models to generate responses that are not only grammatically correct but also contextually relevant and engaging. Metrics designed for these systems typically evaluate factors such as informativeness, relevance, and engagement. For instance, informativeness ensures that the generated text contains all necessary information for the recipient to understand the context and respond appropriately. Relevance checks whether the generated text aligns with the conversation’s topic, avoiding irrelevant or off-topic responses. Engagement evaluates whether the generated text encourages further interaction, maintaining the flow of the conversation [1].

Human-computer interaction also plays a crucial role in communication-based evaluation. Metrics that incorporate user feedback can provide valuable insights into how well the generated text communicates its intent. For example, a metric might measure user satisfaction with the generated text based on a post-interaction survey or a rating scale. Such feedback helps refine NLG models to better meet user expectations and improve the overall quality of the generated text [7]. Additionally, integrating human feedback into the evaluation process allows for a more nuanced assessment of the NLG output’s communicative value, reflecting real-world usage scenarios where human interaction is central.

The advent of large language models (LLMs) has introduced new possibilities for communication-based evaluation. LLMs, such as those highlighted in [2], offer a flexible platform for generating and evaluating NLG outputs. They can be fine-tuned or prompted to perform specific evaluation tasks, allowing for the assessment of various communicative aspects of the generated text. For example, an LLM could be fine-tuned on a dataset of human-rated responses to evaluate the relevance and informativeness of NLG outputs. Alternatively, the LLM could be prompted to generate additional context-specific responses, which can then be compared with the NLG outputs to assess their communicative effectiveness. These approaches enable a more comprehensive evaluation of NLG outputs, considering the broader context of human interaction.

However, communication-based evaluation metrics face several challenges. Variability in human judgment can lead to inconsistent evaluation results, as different evaluators might have varying standards for what constitutes an informative or relevant response. To address this, some metrics employ crowd-sourcing techniques, where multiple evaluators rate the same output, and the average score is taken as the final evaluation. While this approach mitigates individual biases, it does not entirely eliminate the inherent subjectivity in human evaluation [9]. Moreover, the substantial human involvement required for these metrics can be time-consuming and resource-intensive, posing challenges for scalability in large-scale evaluation settings. To overcome this, automated metrics leveraging machine learning techniques to mimic human judgment are being developed. These metrics aim to provide a more efficient yet accurate evaluation of NLG outputs by training on annotated data to predict human ratings [3].

Domain-specific metrics are another critical aspect of communication-based evaluation. Different contexts demand distinct evaluation criteria. For instance, metrics designed for medical contexts would need to consider factors such as medical accuracy and patient comprehension, whereas those for legal contexts might focus on legal correctness and clarity [5]. Developing these metrics requires a careful consideration of unique requirements and constraints, adding complexity but also necessity to the evaluation process.

Despite these challenges, communication-based evaluation metrics offer valuable insights into the communicative effectiveness of NLG systems. By focusing on the quality of communication rather than just surface-level features, these metrics provide a more holistic assessment of NLG outputs. As NLG applications expand into diverse domains, the need for communication-based evaluation metrics will continue to grow, driving further innovation in NLG evaluation methodologies. Future research should aim to develop more robust and adaptable communication-based metrics that can effectively evaluate NLG outputs across a wide range of contexts, ensuring meaningful and effective communication.

### 7.2 PMAN for Question Generation

PMAN, a novel automatic evaluation metric specifically designed for question generation tasks, represents a significant advancement in the evaluation landscape for NLG systems. Unlike traditional metrics like BLEU and ROUGE, which primarily focus on surface-level features such as n-gram overlap and longest common subsequence, PMAN offers a more nuanced assessment by directly measuring the answerability of generated questions. This approach is particularly valuable given the unique challenges and requirements of question generation tasks, which necessitate not only syntactic accuracy but also semantic relevance and logical coherence in the generated questions.

At its core, PMAN evaluates the degree to which a generated question can elicit a meaningful and accurate response, a critical factor often overlooked by traditional metrics. This is accomplished through a combination of automatic evaluation techniques and a carefully designed scoring mechanism that takes into account various aspects of the question, including its informativeness, specificity, and clarity. By grounding the metric in the principle that effective question generation should produce questions that are both answerable and useful, PMAN closely mirrors human evaluators’ criteria, ensuring that the generated questions meet high-quality standards necessary for effective communication.

Compared to metrics like BLEU and ROUGE, PMAN provides a more comprehensive evaluation of question generation tasks. Traditional metrics may yield high scores for questions that appear similar to reference questions on the surface but lack semantic depth or contextual appropriateness. PMAN addresses this issue by focusing on the functional and communicative aspects of the generated questions, thus offering a more precise measure of their utility and effectiveness. This shift in evaluation methodology is especially beneficial in domains such as education and customer service, where the quality and relevance of generated questions can significantly impact user experience and outcomes.

Moreover, PMAN integrates elements of information retrieval and text classification to enhance its evaluation capabilities. By classifying generated questions based on their potential to elicit accurate and informative responses, PMAN provides detailed insights into the strengths and weaknesses of question generation models. This not only aids in refining the models but also supports the identification of common errors and areas for improvement. As a result, PMAN serves as a powerful diagnostic tool for developers and researchers, enabling them to iteratively enhance the quality of generated questions.

In complementarity with traditional metrics like BLEU and ROUGE, PMAN offers a dual perspective on question generation tasks. While these metrics highlight surface-level similarities and structural consistencies, PMAN focuses on the functional aspects, providing a more balanced evaluation of NLG systems. This dual evaluation framework allows for a thorough assessment of question generation models, leading to improved accuracy and reliability in the evaluation process. Additionally, integrating PMAN with existing metrics can help identify the specific areas where models excel or fall short, facilitating targeted improvements and innovation.

PMAN’s adaptability is another key strength. It can be applied across various scenarios, from educational materials and customer service inquiries to knowledge-based systems, making it a versatile tool for different applications. This flexibility is crucial as question generation tasks become more diverse and complex, requiring varied levels of specificity, complexity, and contextual relevance. By accommodating these diverse needs, PMAN enables a more tailored and effective evaluation strategy.

Despite its advantages, PMAN faces challenges such as the need for high-quality reference questions and computational complexity in large-scale evaluations. Addressing these issues is critical for the metric's continued development and broader adoption. Nonetheless, ongoing research aims to refine PMAN, enhancing its robustness and scalability while preserving its alignment with human evaluation standards.

In conclusion, PMAN stands out as a pioneering metric for evaluating question generation tasks within NLG systems. By prioritizing the answerability and communicative efficacy of generated questions, PMAN offers a more thorough and reliable evaluation compared to traditional metrics. Its alignment with human judgment criteria, flexibility, and complementary nature make it a valuable asset for enhancing the quality and effectiveness of question generation systems, contributing to the broader goal of developing more intelligent and user-friendly communication tools.

### 7.3 Synthetic Traffic Generation Evaluation Metrics

Synthetic Traffic Generation (STG) is a specialized domain within NLG focused on creating synthetic texts for Quality Assurance (QA) systems and conversational agents. The primary goal of STG is to produce synthetic data that mirrors the diversity and variability found in real-world communications, essential for testing and validating the robustness and adaptability of QA systems and conversational agents under various conditions.

To effectively evaluate the quality of synthetic traffic, several task-specific metrics have emerged, designed to measure linguistic variability and representativeness. These metrics ensure that the generated texts closely align with real-world scenarios and maintain high relevance and utility.

One such metric is the **Coverage Score**, which evaluates the breadth of linguistic features and scenarios covered by the generated synthetic texts. A high Coverage Score indicates that the dataset includes a wide range of topics, tones, and contexts, suitable for testing the versatility of QA systems and conversational agents.

Another critical metric is the **Relevance Score**, which measures the pertinence of synthetic texts to their intended audience and context. Ensuring that the generated texts are meaningful and reflective of real-world interactions is crucial for the Relevance Score, as it improves the training and validation of QA systems and conversational agents.

The **Fidelity Score** assesses the authenticity of synthetic texts, ensuring they accurately mimic real human interactions. Factors such as natural language usage, appropriate responses, and contextual relevance are evaluated to fine-tune QA systems and conversational agents for recognizing and responding to nuances in human language and behavior.

The **Variability Score** evaluates the diversity and richness of generated synthetic texts. This score promotes a wide range of linguistic styles, structures, and expressions, preventing overfitting to narrow language patterns and enhancing the robustness and adaptability of QA systems and conversational agents.

Finally, the **Consistency Score** assesses the internal logic and coherence of synthetic texts. Ensuring logical flow and thematic coherence prevents contradictions and inconsistencies that could degrade the performance of QA systems and conversational agents, thereby maintaining the credibility and reliability of the synthetic data.

The effectiveness of these metrics is significantly influenced by the quality and representativeness of the input data. Large Language Models (LLMs) have advanced the generation of high-quality synthetic data, closely mirroring real-world interactions. However, the application of LLMs in STG evaluation introduces challenges related to robustness and fairness, highlighting the need for careful validation and refinement of these metrics to ensure reliability and effectiveness.

In summary, these metrics tailored for Synthetic Traffic Generation (STG) ensure that generated texts are linguistically rich, representative, and suitable for testing QA systems and conversational agents. By leveraging the Coverage Score, Relevance Score, Fidelity Score, Variability Score, and Consistency Score, developers can enhance the performance and reliability of QA systems and conversational agents, ultimately improving user-friendly interactions in real-world applications.

### 7.4 DecompEval for Decomposed Question Answering

DecompEval, a groundbreaking evaluation metric, emerges as a pivotal tool in the realm of decomposed question answering, particularly for NLG tasks. Unlike traditional metrics that rely heavily on human-annotated references, DecompEval leverages the capabilities of instruction-tuned pre-trained language models (LLMs) [23] to provide unsupervised evaluations. This innovative approach not only enhances the generalization ability of the evaluation process but also offers unparalleled interpretability, making it a preferred choice for assessing the performance of NLG models in complex, multi-step reasoning tasks.

Central to DecompEval is its use of LLMs that have been fine-tuned on a wide array of instructional prompts, enabling them to understand and evaluate the nuances of generated texts in a decomposed question answering scenario. The primary objective of DecompEval is to assess the quality of generated texts by breaking down the evaluation process into distinct, measurable components. This decomposition allows for a granular inspection of the model’s output, thereby providing deeper insights into its strengths and weaknesses.

One of the standout features of DecompEval is its generalization ability. By harnessing the broad knowledge base of instruction-tuned LLMs, DecompEval can adapt to various decomposed question answering tasks, ranging from straightforward factual retrieval to intricate multi-step reasoning. This adaptability is crucial in the rapidly evolving landscape of NLG, where models are increasingly being deployed in diverse applications, each with its own set of requirements and constraints. The ability of DecompEval to generalize across different tasks underscores its potential as a versatile evaluation tool that can be applied across a spectrum of NLG scenarios.

Moreover, DecompEval’s reliance on instruction-tuned LLMs ensures that the evaluation process remains aligned with the latest advancements in natural language processing. Instruction-tuning has emerged as a powerful technique for enhancing the contextual understanding and reasoning capabilities of LLMs [29]. By leveraging these fine-tuned models, DecompEval can capture the subtleties of generated texts, including the logical coherence and relevance to the given context, which are often overlooked by traditional metrics. This enhanced capability to discern quality makes DecompEval a valuable asset in refining NLG models for decomposed question answering tasks.

Another significant advantage of DecompEval is its interpretability. Unlike black-box evaluation metrics that provide a single score without clear explanations, DecompEval offers a transparent evaluation process. By decomposing the evaluation into multiple components, DecompEval allows evaluators to trace the performance of the model across different dimensions, such as factuality, relevance, and coherence. This level of granularity provides a comprehensive view of the model’s strengths and areas for improvement, facilitating targeted adjustments during the refinement phase. Additionally, the interpretability of DecompEval fosters a deeper understanding of the evaluation criteria, making it easier for stakeholders to align the evaluation process with their specific needs and objectives.

In the context of synthetic traffic generation evaluation metrics [30], DecompEval has proven effective in evaluating the linguistic variability and representativeness of generated texts. Leveraging the generalization ability of instruction-tuned LLMs, DecompEval can handle the diverse and complex nature of synthetic traffic generation, ensuring that the generated texts are not only linguistically varied but also contextually relevant. This capability is particularly important in scenarios where the generated texts must closely mimic real-world conditions, thereby enhancing the realism and utility of the synthetic data.

Furthermore, DecompEval’s focus on multi-step reasoning aligns well with the challenges posed by decomposed question answering tasks. In such tasks, the generated texts often require multiple layers of reasoning to connect the given input to the final output. The ability of DecompEval to break down the evaluation process into discrete steps enables a thorough examination of the reasoning paths followed by the model. This detailed inspection not only helps in identifying potential flaws in the reasoning process but also aids in uncovering the underlying patterns that contribute to the success of the model. Such insights are invaluable for researchers and practitioners looking to optimize the performance of NLG models in complex reasoning tasks.

Despite its numerous advantages, DecompEval is not without limitations. One of the primary challenges faced by DecompEval is the potential for bias introduced by the instruction-tuned LLMs. As noted in the "AlignScore: Evaluating Factual Consistency with a Unified Alignment Function," the performance of LLMs can be influenced by the quality and diversity of the data used during the instruction-tuning process. Therefore, ensuring that the LLMs used in DecompEval are trained on a representative and diverse dataset is crucial to maintaining the fairness and reliability of the evaluation process. Another challenge is the computational cost associated with deploying instruction-tuned LLMs for evaluation purposes. However, advancements in efficient training and deployment strategies for LLMs continue to mitigate these concerns, making DecompEval a viable option for widespread adoption.

Building on the principles discussed in the evaluation of synthetic traffic generation metrics, DecompEval offers a complementary approach to specialized metrics for dialogue response generation. Just as synthetic traffic generation requires coverage, relevance, fidelity, variability, and consistency, dialogue response generation demands context-awareness, coherence, and engagement. DecompEval’s ability to decompose and evaluate generated texts provides a robust framework for addressing these requirements, ensuring that NLG models are rigorously tested and refined for optimal performance in diverse applications.

### 7.5 Dialogue Response Generation Evaluation

Dialogue response generation is a core component of many natural language processing (NLP) applications, including chatbots, virtual assistants, and customer service systems. It involves generating coherent, contextually appropriate responses to user inputs, making the task highly complex and nuanced. Traditional reference-based metrics like BLEU and ROUGE, while widely used in NLG evaluation, often fall short in accurately reflecting the quality and appropriateness of dialogue responses. Building upon the discussion of DecompEval, which emphasizes the importance of context and interpretability in evaluation, this section explores specialized metrics designed for dialogue response generation, highlighting their advantages over traditional metrics and their alignment with human judgments.

Traditional reference-based metrics, such as BLEU and ROUGE, measure the similarity between the generated text and a set of reference texts. These metrics are commonly used due to their simplicity and ease of implementation. However, they suffer from significant limitations when applied to dialogue response generation. One major limitation is their inability to account for the context-dependency of dialogue responses. Responses in a dialogue should not only match the reference text but also align with the conversation's context, making them more suitable and engaging for the user. The "Why We Need New Evaluation Metrics for NLG" paper highlights that such metrics only weakly reflect human judgments of system outputs for data-driven, end-to-end NLG tasks [1]. Consequently, traditional metrics often fail to capture the essence of a dialogue, leading to inaccurate assessments of dialogue response generation systems.

To address these limitations, researchers have developed specialized metrics tailored for dialogue response generation. One such metric is the Language Model Augmented Relevance Score (MARS), which incorporates context awareness into the evaluation process [26]. MARS leverages off-the-shelf language models to generate augmented references that consider both the dialogue context and available human references. By using these augmented references, MARS can score generated text more accurately, rewarding responses that are contextually appropriate and relevant to the conversation. This context-aware approach significantly improves the correlation between MARS and human judgments, demonstrating its superiority over traditional metrics like BLEU and ROUGE.

Another notable metric for dialogue response generation is the Multi-Dimensional Evaluation (UniEval) framework [31]. UniEval redefines NLG evaluation as a Boolean Question Answering (QA) task, allowing for the evaluation of generated text from multiple dimensions, such as coherence and fluency. By framing dialogue response generation as a series of Boolean questions, UniEval captures the multidimensional nature of dialogue, enabling a more comprehensive assessment of response quality. The advantage of UniEval lies in its ability to incorporate external knowledge from multiple related tasks, thereby enhancing its evaluation capabilities. Experimental results indicate that UniEval achieves a 23% higher correlation with human judgments on text summarization and over 43% on dialogue response generation, underscoring its effectiveness in dialogue response generation evaluation.

The emergence of large language models (LLMs) has further transformed the landscape of dialogue response generation evaluation. LLMs, with their extensive training on vast amounts of text data, have the capacity to understand and generate text that aligns closely with human-like responses. However, the reliance on LLMs for evaluation comes with its own set of challenges, including issues related to bias, robustness, and domain-specificity [13]. The paper "Are LLM-based Evaluators Confusing NLG Quality Criteria" reveals that LLMs may confuse different evaluation criteria, leading to unreliable evaluations. For instance, an LLM might assign high scores to responses that excel in one criterion (such as fluency) but fail in others (like relevance). This confusion underscores the need for careful calibration and validation of LLM-based evaluators to ensure their reliability and accuracy.

Building on the principles of DecompEval, which leverages instruction-tuned LLMs for a generalized and interpretable evaluation process, specialized metrics for dialogue response generation aim to achieve similar levels of context-awareness and interpretability. While DecompEval focuses on decomposed question answering tasks, specialized dialogue metrics extend these principles to the broader and more interactive domain of dialogue response generation.

Despite these challenges, LLMs offer promising avenues for dialogue response generation evaluation. The Collaborative Evaluation (CoEval) pipeline proposes a synergy between LLMs and human evaluators to address the limitations of each approach [15]. CoEval utilizes LLMs to generate initial evaluations, which are then refined by human evaluators. This collaborative approach leverages the scalability and efficiency of LLMs while incorporating the nuanced understanding and adaptability of human judgment. Experimental results show that CoEval effectively evaluates lengthy texts, significantly reducing the time required for human evaluations and minimizing outlier scores.

Moreover, specialized metrics like MARS and UniEval, combined with the use of LLMs, present a promising direction for dialogue response generation evaluation. These metrics not only address the limitations of traditional reference-based metrics but also align more closely with human judgments, providing a more holistic assessment of dialogue quality. While challenges remain, ongoing advancements in these areas hold the potential to significantly enhance the evaluation of dialogue response generation systems, ultimately contributing to the development of more effective and user-friendly dialogue systems.

## 8 Human-Centric Evaluation Approaches

### 8.1 Direct Human Judgment Methods

Direct human judgment methods represent a fundamental approach to evaluating the performance of NLG systems, where human evaluators directly assess the quality and appropriateness of generated texts. These methods are widely adopted due to their ability to capture the nuances and subjectivity inherent in natural language, which are often overlooked by purely automated metrics. However, the reliance on human judgments presents significant challenges, particularly in terms of scalability and maintaining consistency across evaluators.

One of the primary advantages of direct human judgment methods is their high reliability. Unlike automatic metrics, which are often criticized for their inability to fully encapsulate the qualitative aspects of generated text, human evaluators can provide a holistic assessment that considers factors such as coherence, fluency, and relevance. These metrics are particularly valuable for tasks that require a deep understanding of context and subtlety, such as dialogue generation or narrative composition [1]. For instance, human judges are adept at recognizing subtle deviations in tone or style that might not be detected by a machine, making them indispensable for evaluating NLG systems aiming to replicate human-like conversational skills.

Additionally, human evaluators can incorporate subjective criteria into their judgments, providing insights that are crucial for assessing the emotional or stylistic impact of NLG outputs. This flexibility allows evaluators to adapt their assessments to the specific goals and requirements of different NLG tasks. For example, in the context of image captioning, human judges can evaluate not only the factual accuracy of captions but also their aesthetic appeal and emotional resonance [17]. This capacity to consider non-technical attributes is essential for creating systems that are not only technically proficient but also engaging and enjoyable for users.

Despite these strengths, direct human judgment methods face considerable challenges, primarily in terms of scalability and consistency. Scalability refers to the ability to efficiently evaluate a large volume of text, a significant hurdle for human-centric approaches. With the rapid advancement in NLG technology, the amount of generated text has increased exponentially, making it impractical to rely solely on human evaluations for large-scale projects. For instance, evaluating millions of lines of text generated by a summarization system would be prohibitively time-consuming and expensive if done entirely by human judges. This issue is exacerbated by the need for consistent evaluation across multiple evaluators, as differences in individual biases and expertise can lead to variability in scoring [3].

Ensuring consistency among human evaluators is another critical challenge. Variability in judgments can arise from differences in evaluator training, experience, and personal preferences. For example, two evaluators assessing the same piece of text might assign significantly different scores based on their interpretation of quality criteria. This inconsistency can undermine the reliability of human judgment methods, leading to unreliable comparisons of NLG system performance. To mitigate these issues, researchers have explored various strategies, such as standardizing evaluation protocols, providing detailed guidelines, and conducting pilot studies to ensure that evaluators are aligned in their assessment criteria [9].

Furthermore, the reliance on human judgment methods raises concerns about the potential for bias and subjectivity in evaluations. While human evaluators bring a rich understanding of language and context, they are also susceptible to cognitive biases that can influence their judgments. For example, evaluators might unconsciously favor outputs that align with their own expectations or preferences, leading to biased assessments [2]. To address this issue, some studies have advocated for the use of double-blind evaluation methods, where evaluators are unaware of the origin of the text, thereby reducing the risk of bias [8]. However, implementing such rigorous evaluation protocols can be resource-intensive and may not always eliminate all forms of bias.

Another limitation of direct human judgment methods is their susceptibility to evaluator fatigue and burnout, especially when dealing with long or repetitive evaluation tasks. Fatigued evaluators may produce inconsistent or less accurate judgments, further compromising the reliability of the evaluation process. To combat this issue, researchers have experimented with various strategies, including breaking down evaluation tasks into smaller segments, rotating evaluators, and providing regular breaks to maintain evaluator engagement and accuracy [5].

In conclusion, while direct human judgment methods offer unparalleled reliability and depth in evaluating NLG systems, they are not without challenges. The scalability and consistency issues pose significant hurdles, particularly in the context of rapidly expanding NLG applications. Nonetheless, the insights gained from human judgments remain invaluable for refining NLG systems and ensuring that they meet the diverse and nuanced demands of real-world applications. Moving forward, integrating human judgments with automated metrics and leveraging advances in technology, such as the emergence of large language models (LLMs), could help address some of these challenges, enabling a more efficient and comprehensive evaluation framework for NLG systems [2].

### 8.2 Hybrid Approaches Combining Human Assessments and Automatic Metrics

Hybrid approaches that integrate human assessments with automatic metrics have emerged as a promising strategy for evaluating NLG systems. These methods aim to harness the strengths of both human evaluators and machine-driven evaluation tools, thereby reducing costs while maintaining or even improving the accuracy of evaluations. By combining human judgments with automatic metrics, these frameworks balance the need for nuanced qualitative insights with the efficiency and objectivity of quantitative measures.

Automated metrics, such as BLEU and ROUGE, commonly used in NLG evaluation, often fall short in capturing the full spectrum of qualities that make a piece of generated text effective and appropriate. Metrics like BLEU and ROUGE focus on surface-level features such as n-gram overlaps and longest common subsequences, which may not fully reflect the semantic coherence or contextual relevance of the generated text [10]. Recognizing this limitation, hybrid approaches incorporate human evaluations to provide deeper insights into the qualitative aspects of NLG outputs.

Relying solely on human assessments can be prohibitively expensive and time-consuming, especially for large-scale NLG projects involving numerous iterations and diverse datasets. Hybrid approaches offer a middle ground by leveraging the strengths of both human and machine-based evaluations. For example, researchers have utilized control variates, a statistical technique designed to mitigate biases in the evaluation process, effectively combined with human evaluations to achieve more accurate and reliable estimates of NLG performance [32].

Notably, the CoAScore approach [33] exemplifies a hybrid method that employs a Chain-of-Aspects prompting technique to evaluate NLG outputs based on multiple criteria simultaneously. CoAScore integrates human evaluations with machine learning-based metrics, allowing for a more comprehensive assessment of NLG systems. This method involves presenting evaluators with prompts that encourage consideration of various aspects of the generated text, such as fluency, relevance, and coherence. Human judgments are then aggregated and combined with automated metrics, offering a richer and more nuanced evaluation than either method alone.

Hybrid approaches enhance the reliability and validity of NLG evaluations by incorporating human assessments, which account for the subjective qualities of NLG outputs often difficult to capture using purely automatic metrics. Human evaluators provide insights into the communicative effectiveness and relevance of generated texts in various contexts, addressing the limitations of traditional metrics [34]. The combination of human and automatic metrics leads to a more balanced evaluation, considering both qualitative and quantitative aspects, thus providing a more holistic assessment of NLG systems.

Moreover, hybrid approaches have been optimized to address specific challenges and requirements of different NLG tasks. For instance, in dialogue response generation, specialized metrics complement traditional reference-based evaluations by focusing on interaction dynamics and communicative effectiveness [11]. Integrating these metrics with human assessments provides a more comprehensive evaluation of dialogue systems, factoring in elements such as turn-taking, context awareness, and the ability to maintain engaging conversations.

Addressing hallucination in NLG is another critical area where hybrid approaches excel. Combining human assessments with machine-driven detection of inconsistencies or factual errors helps identify and mitigate hallucinations more effectively [6]. This dual-pronged approach ensures that NLG systems generate fluent, coherent, and truthful text, meeting real-world application standards.

In addition to enhancing accuracy, hybrid approaches offer opportunities for optimizing resource allocation and cost-efficiency. Preprocessing and filtering NLG outputs using machine learning models allow evaluators to focus on the most relevant or problematic texts, reducing the burden of manual assessment [35]. This targeted approach enables a more efficient use of human resources while ensuring robust and representative evaluations.

The adoption of hybrid approaches also fosters new research and innovation in NLG evaluation. Developing adaptive frameworks that dynamically adjust the weight given to human and automatic assessments based on task-specific characteristics and requirements represents an exciting area of exploration. Such adaptive systems can learn from previous evaluations to refine methodologies, enhancing the overall accuracy and reliability of NLG assessments.

In conclusion, hybrid approaches integrating human assessments with automatic metrics represent a promising direction for NLG evaluation. By leveraging the strengths of both human and machine-driven evaluations, these frameworks offer a more comprehensive and nuanced assessment of NLG systems, while also addressing challenges related to cost-efficiency and resource allocation. As NLG expands into diverse applications, the development and refinement of hybrid evaluation methods will be crucial for ensuring the effectiveness and reliability of generated text across various domains and contexts.

### 8.3 Control Variate Techniques in Human-AI Collaboration

Control variate techniques have emerged as a powerful tool in mitigating biases during the evaluation of NLG systems, particularly when integrating human evaluations with automatic metrics. These techniques leverage auxiliary variables, known as control variates, which are correlated with the primary outcome of interest but have a known expectation, thereby reducing the variance of the estimator and leading to more precise and unbiased estimates of system performance. This section explores how control variate techniques can enhance the evaluation of NLG systems by bridging the gap between human judgments and automated metrics.

One of the main challenges in human-AI collaboration is balancing the qualitative insights provided by human evaluators with the objective measurements derived from automatic metrics. Human judgments, although invaluable for capturing nuanced aspects of NLG outputs, are susceptible to various biases, such as anchoring, confirmation, and recency biases, which can distort evaluation outcomes. Conversely, automatic metrics, while efficient and scalable, may overlook the rich qualitative dimensions of NLG text due to their reliance on surface-level features like n-gram overlaps. Control variate techniques address this imbalance by incorporating auxiliary variables that correlate with the primary evaluation metric, thereby refining the evaluation process.

For example, in assessing the relevance and coherence of generated summaries, human evaluators' judgments can be influenced by factors like the presence of keywords, readability, and summary length. These factors can act as control variates, helping to isolate the true quality of the summary from the influence of these extraneous variables. By integrating these control variates, the variability in human evaluation scores decreases, resulting in more consistent and reliable assessments.

Control variate techniques also address issues like data leakage, a common pitfall in NLG evaluations where information from the test set leaks into the training process, leading to overly optimistic performance estimates. By accounting for potential sources of data leakage, such as the similarity between test and training data, these techniques enhance the robustness of evaluations, ensuring that performance metrics reflect the genuine capabilities of the NLG system.

Additionally, control variate techniques improve the generalizability of evaluation metrics by calibrating them to align more closely with human judgments. Traditional metrics like BLEU and ROUGE, which focus on n-gram overlaps, may perform inconsistently across different domains and tasks. By incorporating control variates that capture more meaningful aspects of generated text, such as grammatical correctness, vocabulary richness, and sentiment polarity, the metrics become more aligned with human perceptions, providing more stable and reliable performance estimates.

Moreover, these techniques facilitate the integration of multiple evaluation criteria into a unified framework. As noted in 'Perturbation CheckLists for Evaluating NLG Evaluation Metrics', NLG evaluation necessitates considering dimensions like fluency, coherence, relevance, and adequacy. Using control variates to adjust for correlations between these dimensions ensures a more holistic assessment, preventing any single criterion from dominating the evaluation.

Implementing control variate techniques involves identifying relevant control variates based on their correlation with the primary evaluation metric and developing a statistical model to incorporate them. Control variates can be derived from input data, generated text, or a combination of both. For instance, in evaluating NLG systems that generate summaries for scientific articles, human judgments on relevance were complemented by control variates like summary length, technical term presence, and readability. This integration yielded more accurate performance estimates by minimizing the impact of confounding factors.

However, the application of control variate techniques comes with challenges. Identifying appropriate control variates requires a thorough understanding of the evaluation task and its influencing factors. Overfitting can occur if too many control variates are included, potentially diminishing the model's generalizability. Additionally, the computational demands of incorporating control variates can be significant, posing limitations in real-time or online evaluation scenarios.

Despite these challenges, control variate techniques offer substantial promise for improving NLG evaluations. By mitigating biases and enhancing evaluation reliability, these techniques contribute to more accurate and trustworthy assessments of NLG performance. Continued research is necessary to optimize the design and implementation of control variate techniques and their integration with other evaluation methods, ultimately supporting the development of more advanced and robust NLG systems.

### 8.4 Human-Centric Metrics for Specific Applications

Human-centric metrics for specific applications of NLG are essential in tailoring evaluation methods to the unique characteristics and requirements of distinct domains and tasks. These metrics not only enhance the relevance and applicability of evaluations but also provide deeper insights into the quality and effectiveness of generated outputs. Two primary areas that benefit significantly from human-centric metrics are climate change image realism and dialogue system output. By leveraging human judgment, these metrics aim to capture nuances and subjective aspects that automated evaluations often overlook.

Climate change image realism represents a growing field within NLG that focuses on generating visual representations of environmental scenarios linked to climate change. These images play a vital role in enhancing public awareness, guiding policy-making, and supporting educational efforts. Given the subjective nature of these images, human-centric metrics are indispensable for assessing their emotional impact, educational value, and overall authenticity.

For instance, human judges could rate the level of detail in generated images, focusing on how accurately they depict elements of climate change, such as sea-level rise, deforestation, or extreme weather conditions. The emotional response evoked by these images is equally important; judges might evaluate how effectively the images convey the seriousness of climate impacts, fostering a sense of urgency and responsibility among viewers.

Moreover, the contextual accuracy of climate change images can be assessed through human-centric metrics. This includes verifying whether the images align with real-world scenarios and data, ensuring they accurately represent climate phenomena without misrepresentation. For example, an image depicting melting glaciers should reflect the actual scale and location of the phenomenon based on scientific evidence.

Dialogue system output is another area where human-centric metrics play a crucial role. These systems, utilized in customer service, virtual assistants, and conversational agents, rely on natural and engaging interactions to deliver satisfactory user experiences. While automated metrics like BLEU and ROUGE offer valuable insights, they often fail to capture the nuances of dialogue quality, such as fluidity, coherence, and relevance.

One approach involves assessing the clarity and informativeness of the dialogue system’s responses. Human judges could evaluate how well the system answers user queries and maintains the relevance of the conversation. Additionally, the naturalness and coherence of the conversation flow can be assessed, with judges considering whether the system’s responses are disjointed or repetitive. The system’s ability to handle unexpected inputs gracefully is also a key factor, ensuring a smooth and engaging dialogue even when faced with unusual or ambiguous questions.

Furthermore, human judges could evaluate the emotional and social dynamics of conversations. This includes assessing the empathy and friendliness exhibited by the dialogue system, gauging its ability to connect with users emotionally. This is especially important for systems designed to provide support or assistance, as a compassionate tone can significantly enhance user satisfaction. The system’s adherence to ethical guidelines, such as avoiding harmful or inappropriate responses, can also be evaluated, ensuring that the dialogue remains respectful and responsible.

Adaptability to changing contexts is another critical aspect that can be assessed through human-centric metrics. The dialogue system should adjust its responses based on evolving contexts, maintaining relevance and coherence. Human judges could evaluate the system’s responsiveness to context shifts, rating its effectiveness in navigating different topics and maintaining coherent interactions.

Moreover, human-centric metrics can incorporate evaluations of the system’s performance in multi-party conversations or complex dialogues involving multiple participants. This includes assessing the system’s ability to manage turn-taking, resolve conflicts, and facilitate collaborative discussions, ensuring that the dialogue remains productive and engaging for all participants.

In summary, human-centric metrics tailored for specific applications such as climate change image realism and dialogue system output offer a nuanced and comprehensive approach to NLG evaluation. By leveraging human judgment, these metrics can capture subjective qualities, emotional impact, and context-specific relevance that automated evaluations often miss. This enhances the relevance and applicability of evaluations, fostering the development of more effective and impactful NLG systems. As NLG continues to expand into diverse domains, the importance of human-centric metrics in ensuring high-quality and contextually appropriate outputs will undoubtedly grow.

### 8.5 Factors Influencing Human Judgment Reliability

The reliability and consistency of human judgments in evaluating NLG systems are influenced by a multitude of factors, including experimental design, participant experience, and task complexity. These elements collectively shape the evaluative process and influence the outcomes, making it imperative to understand their roles in ensuring the validity and robustness of human-centered evaluations.

**Experimental Design**

The structure and design of experiments significantly impact the reliability of human judgments. Factors such as the presentation format, instructions given to participants, and the inclusion of guidelines for evaluation criteria all play pivotal roles in standardizing evaluations. For instance, clear and consistent instructions are essential to ensure that participants are evaluating NLG outputs based on the same criteria, as highlighted in the study "Why We Need New Evaluation Metrics for NLG." Without standardized instructions, there is a risk of introducing variability due to individual interpretation differences, leading to inconsistencies in evaluation outcomes. Additionally, pilot testing to refine instructions and ensure clarity can enhance the reliability of subsequent evaluations.

The selection of representative samples for evaluation is also crucial. Ensuring that the sample includes a diverse set of participants, reflecting a broad spectrum of linguistic abilities and backgrounds, helps mitigate biases and improves the reliability of the evaluation. This is particularly important in cross-cultural evaluations where linguistic nuances and cultural context may influence judgments. For example, "Towards a Unified Multi-Dimensional Evaluator for Text Generation" emphasizes the importance of considering cultural and linguistic diversity in evaluating NLG systems. By incorporating a diverse participant pool, evaluators can capture a wider range of perspectives and reduce the risk of biased evaluations.

**Participant Experience**

The experience and expertise of participants directly affect the reliability and consistency of human judgments. Participants with extensive experience in NLG evaluation are likely to provide more reliable and consistent judgments compared to novices. Familiarity with the NLG system being evaluated, the specific tasks, and the evaluation criteria contributes to the quality of judgments. "Evaluation Discrepancy Discovery: A Sentence Compression Case-study" underscores the importance of participant expertise in minimizing discrepancies in evaluation scores. Thorough training and ensuring that participants understand the evaluation criteria can help minimize variability and enhance the reliability of human judgments.

The cognitive load placed on participants during the evaluation process should also be considered. Overloading participants with too much information or requiring them to evaluate too many samples without breaks can lead to fatigue, which might negatively impact the quality and consistency of their judgments. Providing clear guidelines on how many samples to evaluate per session and ensuring adequate rest periods can help maintain the quality of evaluations. "Collaborative Evaluation: Exploring the Synergy of Large Language Models and Humans for Open-ended Generation Evaluation" highlights the importance of balancing the workload to prevent participant burnout, thereby maintaining the reliability of evaluations.

**Task Complexity**

The complexity of the NLG tasks being evaluated also influences the reliability and consistency of human judgments. Tasks that require a deep understanding of linguistic nuances, contextual awareness, and specialized knowledge are inherently more challenging to evaluate consistently. For example, evaluating the quality of generated dialogue responses in a conversational agent involves assessing not just the grammatical correctness of the response but also its appropriateness in the context of the conversation. "Are LLM-based Evaluators Confusing NLG Quality Criteria" illustrates the complexity of evaluating NLG outputs across multiple criteria, such as fluency, relevance, and coherence, each of which requires distinct evaluative skills. Tasks that are highly complex and multifaceted necessitate a more rigorous evaluation framework to ensure consistency and reliability.

The type of task can further influence the reliability of human judgments. Subjective tasks, such as evaluating the creativity or originality of generated text, are particularly challenging due to their susceptibility to individual biases. Objective tasks, such as evaluating grammatical correctness, tend to yield more consistent judgments. Understanding the inherent subjectivity or objectivity of the task is crucial for designing an evaluation framework that can effectively manage the complexity and ensure reliable outcomes.

In conclusion, the reliability and consistency of human judgments in evaluating NLG systems are multifaceted and influenced by various factors, including experimental design, participant experience, and task complexity. Addressing these factors systematically can enhance the reliability of human-centered evaluations, providing a robust foundation for the assessment of NLG systems. By carefully designing experiments, selecting experienced participants, and accounting for the complexity of tasks, evaluators can mitigate variability and ensure that human judgments accurately reflect the quality of NLG outputs.

## 9 Future Directions and Research Opportunities

### 9.1 Emerging Trends in NLG Evaluation

The field of Natural Language Generation (NLG) evaluation has seen significant advancements in recent years, driven by the growing sophistication of NLG systems and the increasing demand for more nuanced and context-aware assessment methods. One of the most notable trends is the shift from single-aspect evaluation to multi-aspect evaluation, which aims to capture a broader spectrum of qualitative attributes in NLG outputs. Traditional metrics such as BLEU and ROUGE focus primarily on lexical similarity and n-gram overlap, which often fail to measure the semantic richness, coherence, and creativity of NLG outputs. In contrast, multi-aspect evaluation frameworks like CoAScore [1] seek to address these limitations by incorporating a wider range of criteria, including grammatical correctness, semantic accuracy, and stylistic diversity.

CoAScore, introduced by [1], exemplifies this shift. This framework integrates different aspects of NLG outputs—such as syntax, semantics, and style—to generate a more holistic evaluation score. By combining multiple evaluation dimensions, CoAScore provides a more comprehensive assessment of NLG systems, enabling more accurate comparisons and guiding improvements in model development. Multi-aspect evaluation is particularly beneficial for tasks requiring complex reasoning and creative expression, such as text summarization, image captioning, and dialogue generation.

Another significant trend is the increasing use of large language models (LLMs) [7] for NLG evaluation. LLMs excel in capturing intricate language patterns and generating contextually relevant responses, making them invaluable for developing sophisticated evaluation metrics. For instance, the work on Leveraging Large Language Models for NLG Evaluation A Survey highlights the potential of LLMs to assess NLG outputs based on multiple criteria, such as fluency, relevance, and coherence, without relying on human-generated references. This approach not only reduces the need for human annotations but also facilitates more efficient and scalable evaluation processes.

However, integrating LLMs into NLG evaluation presents challenges. One major concern is the robustness of LLM-based metrics against adversarial attacks and spurious correlations [9]. Some LLM-based metrics may rely on superficial features, like word overlap and length, instead of capturing deeper semantic meanings. This issue can result in inaccurate evaluations, especially for NLG systems producing highly creative or context-dependent outputs. Researchers are addressing these challenges by exploring methods to mitigate spurious correlations and enhance metric robustness. For example, the proposal of CheckLists in Perturbation CheckLists for Evaluating NLG Evaluation Metrics introduces a systematic approach to evaluate the stability and reliability of automatic evaluation metrics by perturbing inputs and observing output score changes.

Additionally, LLMs raise questions about evaluation result interpretability and transparency. Unlike traditional metrics like BLEU and ROUGE, which offer clear, straightforward scores, LLM-based metrics generate more complex and abstract evaluations requiring additional interpretation. This complexity can hinder full understanding and trust among practitioners and researchers. To address this, there is growing interest in developing transparent and interpretable evaluation frameworks that leverage LLM strengths while ensuring clarity. For instance, DecompEval [8] proposes a novel method by decomposing the evaluation process into simpler sub-tasks, enhancing interpretability and improving generalization across different tasks and domains.

These trends toward multi-aspect and LLM-based evaluation reflect a broader shift in the NLG evaluation landscape toward more comprehensive and context-aware paradigms. These advancements hold promise for improving the accuracy, reliability, and interpretability of NLG evaluations, ultimately contributing to the development of more effective and robust NLG systems. Realizing the full potential of these new evaluation methods requires further research and refinement to address remaining challenges and limitations. Future efforts should focus on developing more sophisticated multi-aspect evaluation frameworks and improving the robustness, interpretability, and generalizability of LLM-based metrics. Extensive benchmarking and validation studies are also needed to establish the reliability and validity of these new methods across various NLG tasks and domains.

### 9.2 Unmet Needs in Evaluation Strategies

Despite the advancements in evaluation methodologies for Natural Language Generation (NLG) systems highlighted in the previous section, several critical gaps remain in current evaluation practices. One of the primary concerns is the lack of robust methods for assessing faithfulness and creativity, two fundamental aspects of NLG outputs. Faithfulness refers to the degree to which the generated text accurately reflects the input information or context, while creativity involves generating novel and innovative responses. Existing metrics often fall short in capturing these nuances, leading to incomplete evaluations that fail to fully reflect the capabilities of NLG systems.

Faithfulness in NLG is particularly crucial in applications such as abstractive summarization, where the goal is to generate a concise summary that retains the key points and essence of the source document [6]. Current evaluation metrics like ROUGE and BLEU, which rely heavily on lexical overlap and syntactic similarity, struggle to assess whether the generated text faithfully captures the input information. These metrics are insufficient for evaluating the accuracy and completeness of the generated summaries, especially in cases where the generated text diverges significantly from the input content [6].

Similarly, the measurement of creativity poses another significant challenge. Creativity in NLG encompasses the ability to generate novel, unexpected, and valuable content that goes beyond simple paraphrasing or restatement of given information. Traditional metrics that focus on surface-level similarities and n-gram overlaps are inadequate for capturing the innovative nature of creative outputs [34]. Metrics such as BLEU and ROUGE, designed primarily for assessing translation and summarization tasks, tend to penalize divergent yet creative responses, thereby undermining the evaluation of NLG systems designed for creative applications.

Another gap in current evaluation strategies is the need for better handling of diverse languages and cultural contexts. With the increasing globalization and cross-cultural communication facilitated by NLG systems, there is a growing demand for evaluation metrics that can accurately assess the quality of NLG outputs in a wide range of linguistic and cultural settings. However, most existing metrics have been developed and validated primarily in English and Western contexts, leading to potential biases and inaccuracies when applied to non-English languages or cultures [32].

Addressing these challenges requires the development of more sophisticated and culturally sensitive evaluation methods. For instance, incorporating linguistic and cultural knowledge into evaluation metrics could enhance their ability to assess the relevance, appropriateness, and effectiveness of NLG outputs in diverse contexts. Additionally, the inclusion of multilingual benchmarks and datasets in evaluation frameworks would facilitate the validation and comparison of NLG systems across different languages and cultural backgrounds. Such an approach would not only improve the fairness and inclusivity of NLG evaluations but also foster the creation of NLG systems that are more adaptable and responsive to the needs of a global audience.

Furthermore, the emergence of large language models (LLMs) [6] has introduced new opportunities and challenges for NLG evaluation. While LLMs offer the potential for more nuanced and context-aware evaluations, their reliance on vast amounts of training data raises concerns about data bias and the representativeness of the generated text. Ensuring that LLMs are evaluated in a fair and comprehensive manner necessitates the development of robust validation techniques that account for these limitations.

Given these considerations, the integration of human judgment and expertise into NLG evaluation practices becomes increasingly important. Although automatic metrics can provide valuable insights into the performance of NLG systems, they often lack the nuance and depth of human assessments. Combining human evaluations with automatic metrics can lead to more balanced and accurate evaluations, aligning well with the subsequent discussion on enhancing human-machine hybrid assessments. By leveraging the strengths of both human and machine evaluations, researchers and practitioners can gain a deeper understanding of the strengths and weaknesses of NLG systems, facilitating their continuous improvement and refinement.

In conclusion, while significant progress has been made in NLG evaluation, several critical gaps remain in current practices. Addressing these gaps through the development of more robust, culturally sensitive, and integrated evaluation methods is essential for advancing the field of NLG. By focusing on these areas, researchers and practitioners can ensure that NLG systems are evaluated in a comprehensive, fair, and effective manner, paving the way for the continued growth and maturation of NLG technologies.

### 9.3 Enhancing Human-Machine Hybrid Assessments

Enhancing Human-Machine Hybrid Assessments

Integrating human feedback with automatic evaluation methods presents a compelling approach to improving the accuracy and reliability of NLG system assessments. Such hybrid approaches aim to harness the strengths of both human judgment and machine efficiency, leading to more nuanced and comprehensive evaluations. Recent advancements in large language models (LLMs) [3] and reference-free evaluation techniques have paved the way for innovative hybrid systems, yet significant challenges remain in optimizing their performance and utility.

One key aspect of enhancing hybrid assessments lies in refining the methodologies through which human judgments are integrated with automatic metrics. Traditional hybrid approaches often rely on simple aggregation techniques, such as averaging human scores with those generated by automated metrics [7]. While these methods can provide initial insights into the quality of NLG outputs, they often fail to account for the intricate relationship between human and machine evaluations, potentially leading to biased or misleading results.

To address these limitations, researchers have explored more sophisticated frameworks for combining human and machine assessments. One notable method involves the use of control variate techniques, which aim to mitigate biases in human-machine collaboration [22]. Control variates function by incorporating additional variables or metrics that can predict or correlate with human judgments, allowing for the adjustment of human ratings based on these auxiliary measures. This approach not only enhances the accuracy of evaluations but also provides deeper insights into the factors influencing human perceptions of NLG outputs.

Moreover, advancements in machine learning and deep learning have enabled the development of more advanced hybrid systems that learn to combine human and machine evaluations in a data-driven manner. For instance, machine-learned metrics that are trained on paired human-machine evaluations can adaptively weigh and combine the two sources of feedback [7]. These learned metrics can dynamically adjust their weighting based on the specific characteristics of the NLG outputs being evaluated, thereby improving the overall accuracy and reliability of the hybrid assessments.

Another promising direction for enhancing human-machine hybrid assessments is the development of interactive evaluation frameworks that facilitate continuous human-in-the-loop evaluations. Such frameworks enable users to provide iterative feedback on NLG outputs, allowing for the gradual refinement of both human judgments and automated metrics. Interactive evaluation platforms can leverage user interactions to continuously update and optimize the hybrid evaluation process, leading to more robust and adaptive systems.

Furthermore, the integration of large language models (LLMs) into hybrid evaluation frameworks offers significant potential for enhancing the sophistication and depth of NLG evaluations. LLMs can be utilized to generate natural language explanations or rationales for human judgments, facilitating a deeper understanding of the reasoning behind human evaluations [3]. By incorporating these explanatory elements into the evaluation process, hybrid systems can provide richer insights into the strengths and weaknesses of NLG outputs, thereby supporting more informed decision-making in model development and refinement.

However, despite these promising advancements, several challenges remain in optimizing human-machine hybrid assessments. One major issue is the potential for human bias to influence the evaluation outcomes, particularly when human judgments are combined with automated metrics [22]. Ensuring the objectivity and reliability of human evaluations is crucial for maintaining the integrity of hybrid systems. To address this, researchers should focus on developing robust mechanisms for mitigating human bias, such as standardized training procedures for human evaluators and the use of control variates to adjust for known biases.

Another challenge lies in balancing the trade-offs between the efficiency of machine evaluations and the depth of human assessments. While automated metrics can provide rapid and scalable evaluations, they may lack the nuanced understanding and contextual awareness that human evaluators bring to the table [17]? Therefore, developing hybrid systems that effectively blend the strengths of both human and machine evaluations remains a critical research objective.

In conclusion, enhancing human-machine hybrid assessments represents a promising avenue for advancing the evaluation of NLG systems. By refining the methodologies for integrating human feedback with automatic metrics, leveraging advanced machine learning techniques, and incorporating large language models, researchers can develop more sophisticated and reliable evaluation frameworks. Addressing the ongoing challenges in human-machine collaboration and balancing the strengths of both evaluation modalities will be essential for realizing the full potential of hybrid systems in NLG evaluation.

### 9.4 Expanding the Scope of Task-Specific Metrics

Expanding the Scope of Task-Specific Metrics

Task-specific metrics play a crucial role in fine-tuning and validating the performance of Natural Language Generation (NLG) systems in specialized domains. These metrics are tailored to address the unique challenges and requirements of specific NLG tasks, such as text summarization, dialogue generation, and question generation, ensuring that these systems meet the stringent standards of specialized fields like medical and legal writing. This subsection explores potential avenues for enhancing task-specific metrics, focusing on areas that could benefit from more refined and comprehensive evaluation criteria.

One of the primary challenges in developing task-specific metrics lies in the complexity and specificity of the domains in which NLG systems operate. For instance, in medical writing, the precision and accuracy of generated content can directly impact patient care and clinical decision-making. Therefore, metrics for evaluating medical NLG systems must assess not only grammatical correctness and fluency but also factual accuracy, consistency, and adherence to medical guidelines. Similarly, in legal writing, the integrity and coherence of generated documents are paramount, requiring metrics that capture these nuances. Developing such metrics necessitates a deep understanding of the underlying principles and requirements of the respective domains.

To illustrate, consider the challenge of evaluating the accuracy of generated summaries in the medical domain. Current metrics like BLEU and ROUGE, which focus on surface-level features such as n-gram overlap, may fall short in capturing the true quality of summaries due to their inability to assess semantic meaning and contextual relevance [12]. Instead, task-specific metrics for medical summarization might incorporate elements like factual consistency, alignment with medical guidelines, and the ability to convey complex medical information clearly. Such metrics would require the integration of domain-specific knowledge bases and expert annotations to provide a more accurate reflection of summary quality.

Similarly, in the legal domain, task-specific metrics should encompass legal relevance, coherence, and compliance. Legal writing often involves intricate language and requires a high degree of precision and clarity. Therefore, metrics for evaluating legal NLG systems should assess the legal soundness of generated texts, their adherence to legal standards, and their logical consistency. Leveraging large language models (LLMs) trained on extensive legal corpora can identify and penalize inconsistencies and inaccuracies in generated texts [21]. By integrating LLMs into the evaluation process, task-specific metrics can become more robust and capable of capturing the unique characteristics of legal writing.

Moreover, task-specific metrics must be adaptable to the dynamic nature of specialized domains. Medical and legal fields, for example, frequently update regulations, guidelines, and best practices. Metrics designed for these domains must evolve accordingly. Continuous refinement and updating of metrics based on feedback from domain experts and regular validation against updated datasets can ensure relevance and effectiveness. For instance, metrics for medical summarization could be periodically recalibrated using newly released medical literature and expert opinions.

The integration of human evaluations into task-specific metrics also plays a vital role. Automated metrics, while scalable and efficient, often struggle to capture the subjective and qualitative aspects of generated texts. In specialized domains like medical and legal writing, human evaluations can offer valuable insights into the quality and appropriateness of generated content. Combining automated metrics with human assessments can create hybrid evaluation frameworks that leverage the strengths of both approaches. For example, a task-specific metric for legal document generation could utilize LLMs to filter out obvious errors and inconsistencies, followed by a human review process to assess legal soundness and coherence.

Furthermore, task-specific metrics should address the unique challenges associated with specialized NLG tasks. In medical writing, generating summaries or reports from unstructured data poses additional challenges, such as extracting and synthesizing relevant information while maintaining clarity and coherence. Similarly, in legal writing, generating legal documents from complex legal proceedings requires accurately capturing the nuances of legal arguments and evidentiary information. Task-specific metrics should incorporate criteria reflecting the unique requirements of each task.

Finally, enhancing the interpretability and transparency of task-specific metrics is crucial, especially in specialized domains where stakeholders require clear explanations of how generated texts are assessed and why certain scores are assigned. Visualizations or explanations highlighting key features and criteria used in the evaluation can make it easier for users to understand and validate results, fostering greater trust and adoption of NLG systems.

In conclusion, expanding the scope of task-specific metrics for NLG systems in specialized domains such as medical and legal writing presents numerous opportunities for advancing the field. By developing more sophisticated and nuanced metrics, researchers and practitioners can ensure that NLG systems meet the stringent standards required in these domains. This involves integrating domain-specific knowledge, leveraging advanced technologies like LLMs, combining automated and human evaluations, and enhancing interpretability. These efforts will contribute to creating more reliable and effective NLG systems capable of addressing the unique challenges and requirements of specialized tasks.

### 9.5 Advancing the Evaluation of Large Language Models

With the rapid advancements in natural language generation (NLG) technology, large language models (LLMs) have emerged as powerful tools for automating and enhancing NLG evaluations. However, despite their impressive capabilities, LLMs face several challenges that limit their effectiveness in comprehensive and fair evaluations. One of the primary concerns is model bias, where LLMs may exhibit preferences or inaccuracies that skew evaluation outcomes. Additionally, the interpretability of LLM-based evaluations remains low, making it difficult for stakeholders to understand and trust the evaluation process. Furthermore, the ability of LLMs to generalize across different domains and tasks remains a critical area for improvement. In this subsection, we outline potential research directions aimed at addressing these issues and advancing the evaluation of NLG systems using LLMs.

Addressing model bias is crucial for ensuring that LLMs produce unbiased and accurate evaluations. Bias can manifest in various forms, such as gender, racial, or socio-economic biases, which can significantly affect the fairness and reliability of evaluations. To mitigate bias, researchers should focus on developing debiasing techniques that can be integrated into LLMs during the training phase. This involves the use of diverse and representative training datasets that account for various demographic and cultural backgrounds. Additionally, post-hoc debiasing methods can be applied to existing models to adjust their predictions and reduce bias. Recent studies have shown that incorporating fairness constraints during the training of LLMs can help mitigate bias, although more research is needed to understand the long-term effects and potential trade-offs [13].

Enhancing the interpretability of LLM-based evaluations is essential for building trust and understanding among stakeholders. Interpretability encompasses the ability to explain how and why certain evaluations are made, which is critical for users and developers alike. Current LLM-based evaluations often lack interpretability, making it challenging to identify the reasons behind specific evaluation scores. Researchers can explore techniques such as attention mechanisms and saliency maps to provide insights into the decision-making process of LLMs. Attention mechanisms allow for tracking the focus of the model during the evaluation process, revealing which parts of the input text are most influential in shaping the final evaluation. Saliency maps, on the other hand, highlight the importance of individual words or phrases, offering a visual representation of the evaluation process. By improving interpretability, LLMs can become more transparent and trusted, facilitating better collaboration between human evaluators and automated systems [15].

Improving the generalizability of LLMs across different domains and tasks is vital for their widespread adoption in NLG evaluations. While LLMs have shown remarkable performance on specific tasks, their ability to generalize to new and unfamiliar contexts remains limited. To enhance generalizability, researchers can focus on developing more adaptable and flexible models that can learn from diverse data sources and adapt to changing environments. Transfer learning and multi-task learning are two promising approaches that can be explored to improve the generalizability of LLMs. Transfer learning involves pre-training a model on a large dataset and fine-tuning it on smaller, domain-specific datasets. This allows the model to leverage the knowledge gained from the pre-training phase while adapting to the nuances of specific tasks. Multi-task learning, on the other hand, trains a single model to perform multiple related tasks simultaneously, promoting the sharing of knowledge across different tasks and improving overall performance. By fostering adaptability and flexibility, LLMs can become more versatile and effective in evaluating NLG systems across various domains and tasks [31].

Robustness against perturbations and adversarial attacks is another area that requires attention. Perturbations refer to small changes in the input that can significantly alter the output of an LLM, potentially leading to inaccurate evaluations. Adversarial attacks involve deliberate manipulations of the input designed to mislead or confuse the model. Ensuring that LLMs are robust against such perturbations and attacks is crucial for maintaining the integrity and reliability of NLG evaluations. Researchers can develop and apply robustness testing frameworks to systematically evaluate the resilience of LLMs. These frameworks can include a variety of perturbation types and adversarial attack strategies, allowing for a comprehensive assessment of model performance. Additionally, incorporating robustness considerations during the training phase can help strengthen the defenses of LLMs against perturbations and attacks [13].

The integration of human judgment with LLM-based evaluations offers a promising avenue for improving the overall quality and reliability of NLG evaluations. While LLMs offer scalability and efficiency, human evaluations provide depth and nuance that are often lacking in purely automated approaches. Hybrid evaluation frameworks that combine the strengths of both human and LLM evaluations can provide a more balanced and comprehensive assessment of NLG systems. Such frameworks can leverage LLMs for initial screening and filtering of outputs, followed by detailed human review and refinement. This collaborative approach not only enhances the accuracy of evaluations but also fosters a deeper understanding of the strengths and limitations of both human and machine evaluations. By bridging the gap between human and machine evaluations, researchers can create more reliable and trustworthy evaluation systems [15].

In conclusion, advancing the evaluation of NLG systems using LLMs requires a multi-faceted approach that addresses model bias, interpretability, generalizability, robustness, and the integration of human judgment. By tackling these challenges head-on, researchers can unlock the full potential of LLMs in NLG evaluations, paving the way for more accurate, reliable, and trustworthy evaluation practices. As the field continues to evolve, ongoing research and innovation in these areas will be instrumental in driving progress and ensuring the continued success of NLG systems.

### 9.6 Fostering Multilingual and Cross-Cultural Evaluation

Fostering Multilingual and Cross-Cultural Evaluation

The rapid advancement of Natural Language Generation (NLG) systems has led to a proliferation of applications spanning diverse languages and cultural contexts. As these systems become increasingly integrated into global communication platforms, the challenge of ensuring their effectiveness and fairness across different linguistic and cultural environments becomes paramount. However, existing evaluation metrics predominantly focus on English-language systems, leaving a significant gap in the comprehensive assessment of NLG systems in multilingual and multicultural settings. This subsection explores the importance of creating evaluation metrics that are culturally sensitive and linguistically diverse, advocating for research that supports the development of NLG systems that are inclusive and effective across multiple languages and cultural contexts.

One of the primary challenges in fostering multilingual and cross-cultural evaluation lies in the diversity of languages and cultural norms. Each language possesses unique grammatical structures, idiomatic expressions, and cultural nuances that impact the generation and interpretation of natural language. For instance, languages like Arabic, Chinese, and Japanese not only differ in their writing systems but also in their syntactic and semantic structures, making the transfer of evaluation methodologies from one language to another a complex task. Moreover, cultural contexts influence the way people communicate, affecting the relevance and appropriateness of generated content. A message that resonates well in one culture may be considered inappropriate or ineffective in another. Therefore, there is a pressing need for evaluation metrics that can adapt to the distinct characteristics of different languages and cultures.

The emergence of large language models (LLMs) offers a promising avenue for addressing the challenge of multilingual and cross-cultural evaluation. LLMs, by virtue of their massive scale and diverse training datasets, possess the potential to understand and generate text in multiple languages. However, the performance of LLMs can vary significantly across languages due to differences in data availability, language complexity, and the adequacy of training data. For example, languages with limited digital resources, such as certain African languages, may receive less attention in the training of LLMs, leading to poorer performance. Therefore, researchers must develop strategies to ensure that LLMs are trained comprehensively across a wide range of languages and cultural contexts.

To enhance the multilingual and cross-cultural capabilities of NLG systems, it is crucial to incorporate human-centric evaluation methods that can capture the nuances of different linguistic and cultural environments. Direct human judgment methods, which rely solely on human evaluators, offer a valuable approach for assessing the quality of generated text in diverse contexts. However, the scalability and consistency of such methods pose significant challenges, particularly when dealing with a large number of languages and cultures. Hybrid approaches that combine human assessments with automatic metrics provide a more viable solution. By leveraging the strengths of both human and machine evaluations, these hybrid methods can offer a balanced and comprehensive assessment of NLG systems. For instance, the use of control variate techniques can help mitigate biases in human evaluations, ensuring more accurate and fair assessments.

Moreover, the development of task-specific metrics tailored for multilingual and cross-cultural NLG tasks is essential. Such metrics should be designed to capture the unique requirements and challenges of different tasks and languages. For example, in the domain of dialogue systems, the evaluation of response generation must consider cultural norms and conversational etiquette, which can vary widely across different regions and languages. Similarly, for tasks involving the generation of content for specific cultural events or holidays, the metrics should be capable of assessing the cultural sensitivity and appropriateness of the generated text. Researchers must collaborate with linguists and cultural experts to ensure that the evaluation metrics are culturally sensitive and linguistically appropriate.

Another critical aspect of fostering multilingual and cross-cultural evaluation is the integration of multilingual in-context learning (ICL) techniques. ICL, which involves solving tasks using only a few labeled demonstrations, has been shown to be effective in enabling LLMs to learn across different languages and tasks. However, the effectiveness of ICL varies significantly across languages and tasks, highlighting the need for further research into optimizing ICL strategies for multilingual and cross-cultural evaluation. Additionally, the role of demonstrations in ICL remains under-explored in the context of multilingual systems, necessitating a deeper understanding of how to select and structure demonstrations to maximize the performance of LLMs across different languages and cultures.

Furthermore, the development of robust and interpretable evaluation frameworks that can handle the complexity of multilingual and cross-cultural NLG systems is essential. Existing evaluation metrics often struggle with the generalization ability and interpretability required for assessing generated texts in diverse linguistic and cultural contexts. The introduction of metrics like DecompEval, which formulates NLG evaluation as an unsupervised decomposed question-answering task, showcases the potential for developing more robust and interpretable evaluation frameworks. By decomposing the evaluation process into subquestions, these frameworks can provide more detailed insights into the quality of generated texts, facilitating a more nuanced and informed assessment of NLG systems.

In conclusion, fostering multilingual and cross-cultural evaluation is crucial for the advancement of NLG systems in a globalized world. The development of culturally sensitive and linguistically diverse evaluation metrics is essential for ensuring the effectiveness and fairness of NLG systems across different languages and cultural contexts. Researchers must leverage the potential of large language models, integrate human-centric evaluation methods, and develop task-specific metrics tailored for multilingual and cross-cultural tasks. Additionally, the exploration of multilingual in-context learning techniques and the development of robust and interpretable evaluation frameworks will play a vital role in enhancing the evaluation of NLG systems in diverse linguistic and cultural environments. By addressing these challenges, the NLG research community can pave the way for the creation of inclusive and effective NLG systems that meet the diverse needs of users worldwide.


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