WooooDyy/Self-Polish
Codes for the EMNLP 2023 Findings paper "Self-Polish: Enhance Reasoning in Large Language Models via Problem Refining" by Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Tao Gui, Qi Zhang and Xuanjing Huang.
When working with large language models, this helps you refine a problem's wording to get more accurate and converged answers. You provide an initial complex problem, and it iteratively rephrases or clarifies the problem statement until the language model's response stabilizes and is more likely to be correct. Anyone building or evaluating language model applications, such as AI researchers, prompt engineers, or machine learning practitioners, would use this.
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Use this if you need to improve the reasoning capabilities and answer quality of large language models on complex tasks by optimizing the problem statement itself.
Not ideal if you're looking for a tool to fine-tune the language model directly or if your problems are simple and don't require iterative refinement for better answers.
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May 30, 2023
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