Chain-of-Thought Reasoning Without Prompting

• URL: http://arxiv.org/abs/2402.10200v2
• Date: Thu, 23 May 2024 20:53:59 GMT
• Title: Chain-of-Thought Reasoning Without Prompting
• Authors: Xuezhi Wang, Denny Zhou,
• Abstract summary: CoT reasoning paths can be elicited from pre-trained language models by simply altering the textitdecoding process. The presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer.
• Score: 40.92854235219315
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs [48.28847964704554]
  Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks. We propose a novel approach for continuous-space reasoning that does not require modifying the underlying LLM.
  arXiv  Detail & Related papers  (2025-02-17T18:52:29Z)
• Training Large Language Models to Reason in a Continuous Latent Space [84.5618790930725]
  We introduce a new paradigm Coconut (Chain of Continuous Thought) to explore the potential of large language models (LLMs) reasoning in an unrestricted latent space. Experiments show that Coconut can effectively augment the LLM on several reasoning tasks. These findings demonstrate the promise of latent reasoning and offer valuable insights for future research.
  arXiv  Detail & Related papers  (2024-12-09T18:55:56Z)
• To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning [55.52872152909785]
  Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs) We show that CoT gives strong performance benefits primarily on tasks involving math or logic, with much smaller gains on other types of tasks.
  arXiv  Detail & Related papers  (2024-09-18T17:55:00Z)
• Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs [37.147529569445396]
  Tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. Fine-tuning language models (LLMs) leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance. This is achieved through Chain of Preference Optimization (CPO), where LLMs are fine-tuned to align each step of the CoT reasoning paths with those of ToT.
  arXiv  Detail & Related papers  (2024-06-13T14:07:02Z)
• Break the Chain: Large Language Models Can be Shortcut Reasoners [18.047917626825548]
  Chain-of-Thought (CoT) reasoning utilize complex modules but are hampered by high token consumption, limited applicability, and challenges in thinking. This paper conducts a critical evaluation of CoT prompting, extending beyond arithmetic to include complex logical and commonsense reasoning tasks. We propose the integration of human-likes and shortcuts into language models (LMs) through "break the chain" strategies.
  arXiv  Detail & Related papers  (2024-06-04T14:02:53Z)
• Can Separators Improve Chain-of-Thought Prompting? [10.398343318429367]
  Chain-of-thought (CoT) prompting is a simple and effective method for improving the reasoning capabilities of Large Language Models (LLMs) Inspired by human cognition, we introduce COT-SEP, a method that strategically employs separators at the end of each exemplar in CoT prompting.
  arXiv  Detail & Related papers  (2024-02-16T12:46:16Z)
• Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
  We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer. Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
  arXiv  Detail & Related papers  (2024-02-06T03:41:12Z)
• Boosting Language Models Reasoning with Chain-of-Knowledge Prompting [18.326858925174605]
  Chain-of-Knowledge (CoK) prompting aims at eliciting explicit pieces of knowledge evidence in the form of structure triple. Benefiting from CoK, we additionally introduce a F2-Verification method to estimate the reliability of the reasoning chains. Extensive experiments demonstrate that our method can further improve the performance of commonsense, factual, symbolic, and arithmetic reasoning tasks.
  arXiv  Detail & Related papers  (2023-06-10T12:42:36Z)
• Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters [82.84696222087396]
  Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs) We show that CoT reasoning is possible even with invalid demonstrations.
  arXiv  Detail & Related papers  (2022-12-20T05:20:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.