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Research papers on Reasoning in large language models

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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 · 23,439 citations

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

    Xue-Zhi Wang, Jason Wei, Dale Schuurmans, et al. · 2022 · ArXiv · 8,135 citations

    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

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  3. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

    Lei Wang, Wanyu Xu, Yi-Huai Lan, et al. · 2023 · 918 citations

    Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, Few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual efforts, Zero-shot-CoT concatenates the target problem statement with “Let’s think step by step” as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors,

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  4. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

    Qiguang Chen, Li-Bo Qin, Jin-Hao Liu, et al. · 2025 · ArXiv · 352 citations

    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 to fill this

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  5. Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs

    Bowen Jin, Chulin Xie, Jiawei Zhang, et al. · 2024 · 199 citations

    Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnected (e.g., academic papers in a bibliographic graph are linked by citations and co-authorships) which form a (text-attributed) graph. The knowledge in such graphs is encoded not only in single texts/nodes but also in their associated connections. To facilitate the research of augmenting LLMs with graphs, we manually construct a Graph Reasoning Benchmark dataset called GRBench,

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  6. 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 citations

    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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  7. Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

    Xu-Feng Zhao, Meng-Di Li, Wenhao Lu, et al. · 2023 · ArXiv · 90 citations

    Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their reasoning often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. These models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming at improving the zero-shot chain-of-thought reasoning ability of large language models, we propose LoT (Logical Thoughts), a self-improvem

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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 citations

    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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  9. Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models

    Ying-Qian Cui, Peng-Fei He, Jing-Ying Zeng, et al. · 2025 · ArXiv · 49 citations

    Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on challenging tasks. However, the detailed reasoning process in CoT often incurs long generation times and high computational costs, partly due to the inclusion of unnecessary steps. To address this, we propose a method to identify critical reasoning steps using perplexity as a measure of their importance: a step is deemed critical if its removal causes a significant increase in perplexity. Our method enables models to focus solely on generating these critical steps. This can be achieved through two approaches: refini

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  10. Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns

    Yufeng Zhang, Xuepeng Wang, Ling-Xiang Wu, et al. · 2024 · Proceedings of the AAAI Conference on Artificial Intelligence · 43 citations

    Chain of Thought (CoT) prompting can encourage language models to engage in multi-step logical reasoning. The quality of the provided demonstrations significantly influences the success of downstream inference tasks. Current unsupervised CoT methods primarily select examples based on the semantics of the questions, which can introduce noise and lack interpretability. In this paper, we propose leveraging reasoning patterns to enhance CoT prompting effectiveness. Reasoning patterns represent the process by which language models arrive at their final results. By utilizing prior knowledge and prompt-based methods from large models, we first construct task-specific pattern sets. We then select di

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  11. ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing

    Hua-Dai Liu, Jia-Lei Wang, Kaicheng Luo, et al. · 2025 · ArXiv · 40 citations

    While end-to-end video-to-audio generation has greatly improved, producing high-fidelity audio that authentically captures the nuances of visual content remains challenging. Like professionals in the creative industries, this generation requires sophisticated reasoning about items such as visual dynamics, acoustic environments, and temporal relationships. We present ThinkSound, a novel framework that leverages Chain-of-Thought (CoT) reasoning to enable stepwise, interactive audio generation and editing for videos. Our approach decomposes the process into three complementary stages: foundational foley generation that creates semantically coherent soundscapes, interactive object-centric refine

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  12. Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

    Xin-Yu Tang, Xiaolei Wang, Zhi-Hao Lv, et al. · 2025 · 40 citations

    Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capability of long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. This motivates us to investigate whether long CoT reasoning is a general capability for LLMs. In this work, we conduct an empirical analysis for this question from the perspective of representation. We find that LLMs do encode long CoT reasoning as a general capability, with a clear distinction from vanilla CoTs. Furthermore, domain-specific representations are also required for the effecti

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  13. Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models

    Bin Yu, Hang Yuan, Hao-Tian Li, et al. · 2025 · ArXiv · 33 citations

    Recent advances in large language models have demonstrated that Supervised Fine-Tuning (SFT) with Chain-of-Thought (CoT) reasoning data distilled from large reasoning models (e.g., DeepSeek R1) can effectively transfer reasoning capabilities to non-reasoning models. However, models fine-tuned with this approach inherit the"overthinking"problem from teacher models, producing verbose and redundant reasoning chains during inference. To address this challenge, we propose Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning (LS-Mixture SFT), which combines long CoT reasoning dataset with their short counterparts obtained through structure-preserved rewriting. Our experiments demonstrate tha

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  14. CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models

    Feiyang Li, Peng Fang, Zhan Shi, et al. · 2025 · 19 citations

    Chain-of-thought (CoT) reasoning boosts large language models'(LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-generated reasoning chains and lower reasoning performance from natural language prompts compared with code prompts. To address these issues, we propose CoT-RAG, a novel reasoning framework with three key designs: (i) Knowledge Graph-driven CoT Generation, featuring knowledge graphs to modulate reasoning chain generation of LLMs, thereby enhancing reasoning credibility; (ii) Learnable Knowledge Case-aware RAG, which incorporates retrieval-augmented generation (RAG) into knowledge graphs to retrieve relevant sub-cases

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  15. Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models

    Shi-Yu Teng, Jia-Qing Liu, Rahul Kumar Jain, et al. · 2025 · 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · 16 citations

    Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Recent advancements in Large Language Models (LLMs) have shown promise in addressing mental health challenges, including the detection of depression through text-based analysis. However, current LLM-based methods often struggle with nuanced symptom identification and lack a transparent, step-by-step reasoning process, making it difficult to accurately classify and explain mental health conditions. To address these challenges, we propose a Chain-of-Thought Prompting approach that enhances both the performance and interpretability of LLM-based depres

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  16. Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models

    Chanwoo Park, Anna Seo Gyeong Choi, Sunghye Cho, et al. · 2025 · ArXiv · 16 citations

    Societies worldwide are rapidly entering a super-aged era, making elderly health a pressing concern. The aging population is increasing the burden on national economies and households. Dementia cases are rising significantly with this demographic shift. Recent research using voice-based models and large language models (LLM) offers new possibilities for dementia diagnosis and treatment. Our Chain-of-Thought (CoT) reasoning method combines speech and language models. The process starts with automatic speech recognition to convert speech to text. We add a linear layer to an LLM for Alzheimer's disease (AD) and non-AD classification, using supervised fine-tuning (SFT) with CoT reasoning and cue

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  17. Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models

    Rock Yuren Pang, K. Feng, Shang-Bin Feng, et al. · 2025 · Proceedings of the 31st International Conference on Intelligent User Interfaces · 16 citations

    The output quality of large language models (LLMs) can be improved via “reasoning”: generating segments of chain-of-thought (CoT) content to further condition the model prior to producing user-facing output. While these chains contain valuable information, they are verbose and lack explicit organization, making them tedious to review. Moreover, they lack opportunities for user feedback, such as removing unwanted considerations, adding desired ones, or clarifying unclear assumptions. We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics and enables user review and modification. We implement interactive reasoning in Hippo, a

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  18. Corvid: Improving Multimodal Large Language Models Towards Chain-of-Thought Reasoning

    Jing-Jing Jiang, Chao Ma, Xu-Rui Song, et al. · 2025 · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · 15 citations

    Recent advancements in multimodal large language models (MLLMs) have demonstrated exceptional performance in multimodal perception and understanding. However, leading open-source MLLMs exhibit significant limitations in complex and structured reasoning, particularly in tasks requiring deep reasoning for decision-making and problem-solving. In this work, we present Corvid, an MLLM with enhanced chain-of-thought (CoT) reasoning capabilities. Architecturally, Corvid incorporates a hybrid vision encoder for informative visual representation and a meticulously designed connector (GateMixer) to facilitate cross-modal alignment. To enhance Corvid's CoT reasoning capabilities, we introduce MCoT-Inst

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  20. BadThink: Triggered Overthinking Attacks on Chain-of-Thought Reasoning in Large Language Models

    Shuai-Tong Liu, Ren-Jue Li, Li-Jia Yu, et al. · 2025 · ArXiv · 11 citations

    Recent advances in Chain-of-Thought (CoT) prompting have substantially improved the reasoning capabilities of large language models (LLMs), but have also introduced their computational efficiency as a new attack surface. In this paper, we propose BadThink, the first backdoor attack designed to deliberately induce "overthinking" behavior in CoT-enabled LLMs while ensuring stealth. When activated by carefully crafted trigger prompts, BadThink manipulates the model to generate inflated reasoning traces—producing unnecessarily redundant thought processes while preserving the consistency of final outputs. This subtle attack vector creates a covert form of performance degradation that significantl

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  23. Discourse Structure as an Interpretable Signal for Detecting Hallucinated Chain-of-Thought Reasoning in Large Language Models

    Boris Galitsky · 2026 · AI

    Large language models can generate fluent chain-of-thought (CoT) reasoning that appears coherent while exhibiting systematic distortions in evidence weighting and hypothesis comparison. This paper studies hallucinated CoT as a discourse-structural phenomenon, not only a factual one. We introduce a diagnostic reasoning benchmark with paired grounded and hallucinated explanations, where traces differ in how they organize evidence, alternatives, and defeaters. We extract discourse tree features that summarize evidence allocation, contrast preservation, commitment timing, and evidence integration, and combine them with the Joint Knowledge–Reasoning Hallucination Measure (JKRHM). Experiments on t

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  24. Methods for Enhancing the Stability and Robustness of Chain-of-Thought Reasoning in Large Language Models

    Zhen Xu · 2026 · Journal of Computer Technology and Electronic Research

    Chain-of-Thought reasoning in large language models enhances the ability to solve complex tasks by generating intermediate steps; however, its stability and robustness face significant challenges. The reasoning process is susceptible to internal randomness, knowledge ambiguity, and error propagation, while existing architectures lack effective mechanisms for process constraint and regulation. This paper systematically analyzes the inherent vulnerabilities of such reasoning and proposes stability optimization methods, including structured iterative refinement, multi-granularity consistency constraints, and dynamic uncertainty regulation. It constructs a robustness enhancement framework encomp

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