Summary of Putting People in Llms’ Shoes: Generating Better Answers Via Question Rewriter, by Junhao Chen and Bowen Wang and Zhouqiang Jiang and Yuta Nakashima
Putting People in LLMs’ Shoes: Generating Better Answers via Question Rewriter
by Junhao Chen, Bowen Wang, Zhouqiang Jiang, Yuta Nakashima
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed question rewriter uses single-round instance-level prompt optimization to enhance the intelligibility of human questions for large language models, improving the quality of generated answers. The approach optimizes the rewriter using direct preference optimization based on feedback from automatic criteria evaluating generated answers, eliminating the need for costly human annotations. Experiments across multiple black-box LLMs and long-form question answering datasets demonstrate the efficacy of the method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a practical framework for training question rewriters and sets a precedent for future explorations in prompt optimization within long-form question answering tasks. The goal is to improve the quality of generated answers by making user questions more intelligible for large language models. |
Keywords
» Artificial intelligence » Optimization » Prompt » Question answering
