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Papers on “large language models reasoning chain of thought”

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  1. Chain of Thought Prompting Elicits Reasoning in Large Language Models

    Jason Wei, Xue-Zhi Wang, Dale Schuurmans, et al. · 2022 · ArXiv · 23,000 cites

    We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking. For instance, prompting a 540B-parameter language model with just

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  2. Large Language Models are Zero-Shot Reasoners

    Takeshi Kojima, Shi-Xiang Gu, Machel Reid, et al. · 2022 · ArXiv · 8,278 cites

    Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding"Let's think step by step"before each answer. Experime

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  3. Self-Consistency Improves Chain of Thought Reasoning in Language Models

    Xue-Zhi Wang, Jason Wei, Dale Schuurmans, et al. · 2022 · ArXiv · 8,015 cites

    Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer. Our extensive empirical evaluation shows that self-consistency boosts the

  5. Towards reasoning era: a survey of long chain-of-thought for reasoning large language models

    Qiguang Chen, Libo Qin, Jinhao Liu, et al. · 2026 · Science China Information Sciences · 350 cites

    Abstract Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like “overthinking” and “inference-time scaling”. This survey seeks

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  7. Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

    Yao Fu, Litu Ou, Mingyu Chen, et al. · 2023 · ArXiv · 137 cites

    As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons: (1) from the behavior of GPT and PaLM model family, we observe that complex reasoning is likely to be a key differentiator between weaker and stronger LLMs; (2) we envisage large language models to become the next-generation computational platform and foster an ecosystem of LLM-based new applications, this naturally requires the foundation models to perform complex tasks that

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  8. Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?

    Yancheng He, Shi-Long Li, Jiaheng Liu, et al. · 2025 · ArXiv · 58 cites

    Recently, o1-like models have drawn significant attention, where these models produce the long Chain-of-Thought (CoT) reasoning steps to improve the reasoning abilities of existing Large Language Models (LLMs). In this paper, to understand the qualities of these long CoTs and measure the critique abilities of existing LLMs on these long CoTs, we introduce the DeltaBench, including the generated long CoTs from different o1-like models (e.g., QwQ, DeepSeek-R1) for different reasoning tasks (e.g., Math, Code, General Reasoning), to measure the ability to detect errors in long CoT reasoning. Based on DeltaBench, we first perform fine-grained analysis of the generated long CoTs to discover the ef

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