Title: Chain-of-Thought Reasoning Without Prompting
Authors: Xuezhi Wang, Denny Zhou
Year: 2024
arXiv: 2402.10200 (https://arxiv.org/abs/2402.10200)

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.
