
# USER: 
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│  > There are some documents in summary.txt. May I ask how the evaluation methods for the quality and effectiveness of ideas are conducted in these papers?  │
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# AGENT: 


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 │ ✓  ReadFile summary.txt  │
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✦ Among these papers, the evaluation methods for the quality and effectiveness of ideas vary widely and can be mainly summarized into the following categories:

   1. Quantitative evaluation based on multi-dimensional metrics:
       * Diversity, Novelty, and Correctness (DNC) Framework: As proposed in Paper 1 and Paper 11 (content duplication), the DNC framework measures diversity through indicators such as MTLD, TF-IDF, semantic diversity of embeddings (SDE), Self-BLEU, and lexical entropy; novelty is treated as a natural language inference (NLI) task, judged using models like GPT-4o and SBERT; correctness is assessed by GPT-4o for relevance and DeepSeekV3 for linguistic structure and grammatical accuracy.
       * distinctk and utilityk metrics: Paper 12 introduces the NOVELTYBENCH benchmark, where distinctk measures the number of independent categories in generated content, while utilityk combines diversity and quality, quantified through equivalence classifiers (DeBERTa fine-tuned) and reward models (Skywork-Reward-Gemma-2-27B-v0.2).

   2. Subjective evaluation by human experts:
       * Multi-dimensional scoring: Papers 3 (SciPIP) and 4 (CAN LLMs GENERATE NOVEL RESEARCH IDEAS?) both use human experts (AI researchers, NLP researchers) to subjectively score generated ideas across dimensions including novelty, clarity, feasibility, relevance, excitement, expected effectiveness, and overall scores (typically on a 0-5 or 1-10 scale).
       * Paired Comparison: Papers 3 and 4 also employ paired comparison (Win Rate Comparison), allowing human experts to directly compare the quality of ideas generated by different methods.

   3. Novelty evaluation based on Retrieval-Augmented Generation (RAG):
       * LLM-driven retrieval and scoring: Paper 2 (Harnessing Large Language Models for Scientific Novelty Detection) proposes a RAG-based strategy that uses a trained idea retriever (LLM-KD framework) to retrieve relevant ideas, followed by cross-checking and novelty scoring by an LLM (such as deepseek-reasoner), ultimately classified through a decision tree.
       * Context-aware novelty judgment: Paper 5 (Evaluating and Enhancing Large Language Models for Novelty Assessment) introduces the SchNovel benchmark and RAG-Novelty method, where an LLM determines which of two papers is more novel, incorporating external knowledge (average publication year of retrieved similar papers) as a basis for judgment.
       * Literature-grounded novelty assessment: Paper 7 (Literature-Grounded Novelty Assessment of Scientific Ideas) and Paper 10's "Idea Novelty Checker" module use multi-stage retrieval and facet-based LLM reranking, enabling the LLM to classify ideas as novel or non-novel with explanatory reasons, supported by relevant literature.

   4. Evaluation based on graph structures and semantic embeddings:
       * Relative Neighborhood Density (RND) algorithm: Paper 6 (ENABLING AI SCIENTISTS TO RECOGNIZE INNOVATION) proposes the RND algorithm to evaluate novelty by comparing the local density of an idea with the local density of its neighborhood, using AUROC as the evaluation metric.
       * Graph Neural Network (GNN) evaluation: Paper 8 (GRAPHEVAL) introduces the GraphEval framework, which decomposes complex ideas into "concepts" and builds a "concept graph," then uses a graph neural network (GraphEval-GNN) to predict evaluation scores, integrated with a plagiarism detection mechanism.

   5. Evaluation based on latent space exploration:
       * Evaluator LLM: Paper 9 (Large Language Models as Innovators) proposes a framework that includes an "Evaluator LLM" (such as GPT-4o) to score and filter ideas generated through latent space exploration, with evaluation criteria including originality, relevance, elaboration, fluency, and flexibility.

   6. Evaluation in multi-agent systems:
       * API-driven scoring: Paper 13's (SCIAGENTS) "Novelty Assistant" agent uses the Semantic Scholar API to search for relevant literature, analyzes titles and abstracts, and provides novelty and feasibility scores (1-10).

  In summary, these papers generally combine quantitative metrics (diversity, novelty, correctness, F1 scores, etc.), subjective judgment by human experts, reasoning and classification capabilities of LLMs (often enhanced through RAG), and advanced algorithms based on semantic embeddings and graph structures when evaluating idea quality and effectiveness. Among these, novelty is one of the core focuses of evaluation, and many methods emphasize the interpretability of evaluation results, that is, providing the basis for judgments.

# USER: 
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│  > What I need is not algorithm design, but the evaluation metrics in its experimental design.  │
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# AGENT: 



✦ Okay, I understand. You need the specific evaluation metrics used in these papers' experimental designs to assess the effectiveness of idea quality.

Here are the evaluation metrics used in each paper:

1. Multi-Novelty-Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming (Paper 1 & Paper 11):
   * Diversity: MTLD (Measure of Textual Lexical Diversity), SDT (Semantic Diversity of Text, based on TF-IDF), SDE (Semantic Diversity of Embeddings, based on BERT embeddings), Self-BLEU (converted to diversity metric), Lexical Entropy.
   * Novelty: Determined using GPT-4o and SBERT as novelty detectors (based on NLI task).
   * Correctness: Accuracy (assessing relevance), linguistic structure and grammatical accuracy.

2. Harnessing Large Language Models for Scientific Novelty Detection (Paper 2):
   * Retrieval task: Acc@k (k={1, 5, 10, 20, 50}), MAP (Mean Average Precision).
   * Novelty Detection (ND) task: Accuracy, Precision, Recall, F1-score.

3. SciPIP- An LLM-based Scientific Paper Idea Proposer (Paper 3):
   * Subjective evaluation: Novelty, Clarity, Feasibility, Relevance, Helpfulness (0-5 scale).
   * Pairwise comparison: Win Rate Comparison.

4. CAN LLMS GENERATE NOVEL RESEARCH IDEAS? A LARGE-SCALE HUMAN STUDY WITH 100+ NLP RESEARCHERS (Paper 4):
   * Human reviewer ratings: Novelty, Excitement, Feasibility, Expected Effectiveness (1-10 scale), Overall Score.

5. Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications (Paper 5):
   * Accuracy.

6. ENABLING AI SCIENTISTS TO RECOGNIZE INNOVATION- A DOMAIN-AGNOSTIC ALGORITHM FOR ASSESSING NOVELTY (Paper 6):
       * AUROC (Area Under the Receiver Operating Characteristic Curve)。

   7. Literature-Grounded Novelty Assessment of Scientific Ideas (Paper 7):
       * Accuracy, Precision, Recall, F1 Score, Cohen Kappa.

   8. GRAPHEVAL- A LIGHTWEIGHT GRAPH-BASED LLM FRAMEWORK FOR IDEA EVALUATION (Paper 8):
       * Accuracy, Macro Precision, Macro Recall, Macro F1 Score, Token Cost, Normed Cost.

   9. Large Language Models as Innovators- A Framework to Leverage Latent Space Exploration for Novelty Discovery (Paper 9):
       * Originality, Elaboration, Fluency, Flexibility (scored by evaluator LLM).

   10. Scideator- Human-LLM Scientific Idea Generation and Novelty Evaluation Grounded in Research-Paper Facet Recombination (Paper 10):
       * User Study: Various metrics from the Creativity Support Index (CSI) questionnaire (such as explorability, control, transparency, sense of outcome value, immersion, etc.).
       * Novelty Checker: Accuracy, Recall (especially accuracy in predicting "not novel").

   11. 574_NoveltyBench_Evaluating_Cr (Paper 12):
       * distinctk: Measures the number of functionally distinct categories.
       * utilityk: Cumulative utility metric combining diversity and quality.
       * Equivalence Classifier Performance: Accuracy, F1 Score.

   12. SCIAGENTS- AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING (Paper 13):
       * Novelty and Feasibility scores (1-10 scale) (provided by "Novelty Assistant" agent through analysis via Semantic Scholar API).
       * Case Study: Qualitative demonstration through specific generated hypotheses and expected outcomes.