三思而后行：问题细化提示提升大型语言模型的数学推理能力
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
AI总结：
本文提出问题细化提示（PEP）方法，通过在推理前分解和阐释问题上下文来增强LLM的数学能力，实验证明该方法在多种任务中表现优异且易于集成。
摘要：
大型语言模型（LLMs）在处理数学推理等复杂任务时仍然面临困难。尽管在改进前缀提示或推理过程方面投入了大量精力，但问题上下文的关键作用可能被忽视了。准确识别输入是解决数学任务的基础，因为格式不良的问题可能会误导LLM的推理。在本研究中，我们提出了一种名为问题细化提示（PEP）的新方法来增强LLM的数学能力。具体而言，PEP在推理之前对问题上下文进行分解和阐释，从而增强上下文建模和解析效率。跨数据集和模型的实验表明了其出色的性能：（1）PEP在各种数学任务中展现了整体提升。例如，使用GPT-3.5模型，PEP在GSM8k上通过贪婪解码和自洽性分别取得了9.93%和8.80%的提升。（2）PEP可以轻松实现并与其他提示方法集成。（3）PEP在处理干扰问题方面表现出特别的优势。
英文摘要：
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, the crucial role of problem context might have been neglected. Accurate recognition of inputs is fundamental for solving mathematical tasks, as ill-formed problems could potentially mislead LLM's reasoning. In this study, we propose a new approach named Problem Elaboration Prompting (PEP) to enhance the mathematical capacities of LLMs. Specifically, PEP decomposes and elucidates the problem context before reasoning, therefore enhancing the context modeling and parsing efficiency. Experiments across datasets and models demonstrate promising performances: (1) PEP demonstrates an overall enhancement in various mathematical tasks. For instance, with the GPT-3.5 model, PEP exhibits improvements of 9.93% and 8.80% on GSM8k through greedy decoding and self-consistency, respectively. (2) PEP can be easily implemented and integrated with other prompting methods. (3) PEP shows particular strength in handling distraction problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
