Chain-of-Thought Reasoning Without Prompting
<|reference_start|>Chain-of-Thought Reasoning Without Prompting: 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.<|reference_end|>
@article{wang2024chain-of-thought,
    title={Chain-of-Thought Reasoning Without Prompting},
    author={Xuezhi Wang, Denny Zhou},
    journal={arXiv preprint arXiv:2402.10200},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10200},
    primaryClass={cs.CL}
}