title: Chain-of-Thought Reasoning Without Prompting
summary: 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 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' 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.
paper_authors._id: 65cedd98d108e608cabc6d9e
paper_authors.name: Xuezhi Wang
paper_authors._id: 65cedd98d108e608cabc6d9f
paper_authors.name: Denny Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-20T18:25:59.979Z
paper_authors._id: 65d4d62d493aeaf0bb295f1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
paper_authors.fullname: Denny Zhou
paper_authors.type: user
paper_authors.user: dennyzhou
paper_title: Chain-of-Thought Reasoning Without Prompting
paper_summary: 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 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' 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.
paper_ai_keywords: large language models (LLMs) few-shot zero-shot chain-of-thought (CoT) prompting decoding process top-$k$ alternative tokens intrinsic reasoning abilities confidence metric reasoning benchmarks greedy decoding