Computer Science > Computation and Language
[Submitted on 23 Apr 2024 (v1), last revised 28 Apr 2024 (this version, v2)]
Title:Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Reasoners
View PDF HTML (experimental)Abstract:Chain of Thought prompting strategy has enhanced the performance of Large Language Models (LLMs) across various NLP tasks. However, it still has shortcomings when dealing with complex reasoning tasks, including understanding errors, calculation errors and process errors (e.g., missing-step and hallucinations). Subsequently, our in-depth analyses among various error types show that deeply understanding the whole problem is critical in addressing complicated reasoning tasks. Motivated by this, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to enhance the LLMs' reasoning abilities. The core of our method is to encourage the LLMs to deeply understand the problems and leverage the key problem-solving information for better reasoning. Extensive experiments on 10 diverse reasoning benchmarks show that our DUP method consistently outperforms the other counterparts by a large margin. More encouragingly, DUP achieves a new SOTA result on the GSM8K benchmark, with an accuracy of 97.1% in a zero-shot setting.
Submission history
From: Kang Wang [view email][v1] Tue, 23 Apr 2024 12:16:05 UTC (1,214 KB)
[v2] Sun, 28 Apr 2024 15:55:52 UTC (1,231 KB)
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