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Computer Science > Computation and Language

arXiv:2402.10200 (cs)
[Submitted on 15 Feb 2024]

Title:Chain-of-Thought Reasoning Without Prompting

Authors:Xuezhi Wang, Denny Zhou
Download a PDF of the paper titled Chain-of-Thought Reasoning Without
Prompting, by Xuezhi Wang and 1 other authors
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    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 substantially outperforms the
    standard greedy decoding.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2402.10200 [cs.CL]
          (or arXiv:2402.10200v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2402.10200
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Xuezhi Wang [view email]
[v1] Thu, 15 Feb 2024 18:55:41 UTC (752 KB)
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