Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models

Large language models~(LLMs) have exhibited impressive performance across NLP tasks. So far they still face challenges in complex reasoning tasks and can be sensitive to input context. Despite significant efforts have been invested in enhancing reasoning process and improving prefix-prompts robustness, the crucial role of problem context has been overlooked. In this study, we propose a new approach to improve the mathematical capacities of LLMs, named Problem Elaboration Prompting~(PEP). Specifically, PEP decomposes and elucidates the problem context before reasoning, thus enhancing the global context modeling and reducing the parsing difficulties. Experiments on datasets demonstrate promising performances on complex reasoning and indicate the beneficial impact for ill-formed problems. For instance, with the GPT-3.5 model~(\texttt{text-davinci-003}), we observed a 9.93\% improvement with greedy decoding and 8.80\% improvement with self-consistency on GSM8k compared to the standard CoT. With ChatGPT~(\texttt{turbo}) and PEP, we achieve SOTA performances on SVAMP with 86.2\% and GSM8k with 90.98\%.