Chain-of-Thought Reasoning Without Prompting

• URL: http://arxiv.org/abs/2402.10200v2
• Date: Thu, 23 May 2024 20:53:59 GMT
• Title: Chain-of-Thought Reasoning Without Prompting
• Authors: Xuezhi Wang, Denny Zhou,
• Abstract summary: CoT reasoning paths can be elicited from pre-trained language models by simply altering the textitdecoding process. The presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer.
• Score: 40.92854235219315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the \textit{decoding} process. Rather than conventional greedy decoding, we investigate the top-$k$ alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' \textit{intrinsic} reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding effectively elicits reasoning capabilities from language models, which were previously obscured by standard greedy decoding.

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