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.

大规模语言模型的推理能力可通过修改解码过程有效地引出连续推理路径，而不需要手动设计提示技术，这种具备内在推理能力的解码方法优于传统贪婪解码方式。