给大模型提示解题思路，能显著提升数学难题求解能力。
Give me a hint: Can LLMs take a hint to solve math problems?
- 通过类人类教学的提示策略增强模型推理能力
- 在MATH数据集上显著优于单次、少样本及思维链提示
- 揭示提示敏感性，适合研究模型可解释性与教学法
尽管顶尖大语言模型在逻辑和基础数学推理方面表现不佳，近期研究尝试通过提示技巧提升其解题能力。本文受人类教学方式启发，提出向模型提供‘提示’以增强其解决高阶数学问题的能力，并测试了对抗性提示下的鲁棒性，发现模型对此类提示存在敏感性。我们通过评估多种多样大模型，在涵盖不同难度与主题的MATH数据集上验证了该方法的有效性，并与单次提示、少样本提示及思维链提示等技术进行对比。
原文摘要 · Abstract (English)
While state-of-the-art LLMs have shown poor logical and basic mathematical reasoning, recent works try to improve their problem-solving abilities using prompting techniques. We propose giving "hints" to improve the language model's performance on advanced mathematical problems, taking inspiration from how humans approach math pedagogically. We also test robustness to adversarial hints and demonstrate their sensitivity to them. We demonstrate the effectiveness of our approach by evaluating various diverse LLMs, presenting them with a broad set of problems of different difficulties and topics from the MATH dataset and comparing against techniques such as one-shot, few-shot, and chain of thought prompting.
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