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arXiv · 2401.11467

Over-Reasoning and Redundant Calculation of Large Language Models

Cheng-Han Chiang·Hung-yi Lee
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Abstract

Large language models (LLMs) can solve problems step-by-step. While this chain-of-thought (CoT) reasoning boosts LLMs' performance, it is unclear if LLMs \textit{know} when to use CoT and whether those CoT are always necessary to answer the question. This paper shows that LLMs tend to generate redundant calculations and reasoning on a manually constructed math QA dataset, GSM8K-Zero. GSM8K-Zero is constructed such that the questions can be answered without any calculations, but LLMs, including Llama-2 models and Claude-2, tend to generate lengthy and unnecessary calculations to answer the questions. We also conduct experiments to explain why LLMs generate redundant calculations and reasonings. GSM8K-Zero is publicly available at https://github.com/d223302/Over-Reasoning-of-LLMs and https://huggingface.co/datasets/dcml0714/GSM8K-Zero.

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Cheng-Han Chiang, Hung-yi Lee. 2024-01-21. Over-Reasoning and Redundant Calculation of Large Language Models. https://arxiv.org/abs/2401.11467

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