Improving Generation with Large Language Models through Strategic Comparisons
Abstract: Large Language Models (LLMs) have shown advanced capabilities in tasks like counterfactual generation and style transfer using prompt strategies. However, previous strategies lacked detailed instructions, limiting effectiveness. To address this, we introduce Compare&Generate, an algorithm inspired by human comparison, where minimal instructions lead to substantial learning. Specifically, our method incorporates an objective function that quantitatively assesses alignment with the task goal and the content relevance in the output. Then, it constructs comparison pairs based on previous generation assessments and prompts the model to reconsider how to optimize its output. Through comparison, the model focuses on the critical aspects of the task objective and refines its outputs accordingly. We benchmark our method with single-instruction as well as iterative refinement approaches across three natural language generation tasks. Experimental results show that our approach outperforms other related methods; for instance, it surpasses its single-instruction base by 17% and a state-of-the-art refinement approach by 7% on IMDB datasets in generated label accuracy, highlighting the effectiveness of using comparisons in prompts to enhance LLMs.
Paper Type: Long
Research Area: Generation
Research Area Keywords: few-shot generation, text-to-text generation, inference methods, automatic evaluation,
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 94
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