How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning

• URL: http://arxiv.org/abs/2402.18312v2
• Date: Mon, 6 May 2024 09:16:15 GMT
• Title: How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
• Authors: Subhabrata Dutta, Joykirat Singh, Soumen Chakrabarti, Tanmoy Chakraborty,
• Abstract summary: A lack of understanding prevails around the internal mechanisms of the models that facilitate Chain-of-Thought (CoT) prompting. This work investigates the sub-structures within Large Language Models that manifest CoT reasoning from a point of view.
• Score: 44.02173413922695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite superior reasoning prowess demonstrated by Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails around the internal mechanisms of the models that facilitate CoT generation. This work investigates the neural sub-structures within LLMs that manifest CoT reasoning from a mechanistic point of view. From an analysis of Llama-2 7B applied to multistep reasoning over fictional ontologies, we demonstrate that LLMs deploy multiple parallel pathways of answer generation for step-by-step reasoning. These parallel pathways provide sequential answers from the input question context as well as the generated CoT. We observe a functional rift in the middle layers of the LLM. Token representations in the initial half remain strongly biased towards the pretraining prior, with the in-context prior taking over in the later half. This internal phase shift manifests in different functional components: attention heads that write the answer token appear in the later half, attention heads that move information along ontological relationships appear in the initial half, and so on. To the best of our knowledge, this is the first attempt towards mechanistic investigation of CoT reasoning in LLMs.

Related papers

• Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment [54.62926010621013]
  We introduce a novel task, code reasoning, to provide a new perspective for the reasoning abilities of large language models. We summarize three meta-benchmarks based on established forms of logical reasoning, and instantiate these into eight specific benchmark tasks. We present a new pathway exploration pipeline inspired by human intricate problem-solving methods.
  arXiv  Detail & Related papers  (2025-02-17T10:39:58Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in Multi-Step Reasoning [26.79907640964047]
  We investigate the internal reasoning mechanism of language models during symbolic multi-step reasoning. We find that simple subproblems are solved before chain-of-thought begins, and more complicated multi-hop calculations are performed during CoT.
  arXiv  Detail & Related papers  (2024-12-02T04:35:54Z)
• Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning [8.609587510471943]
  We introduce a novel and interpretable analysis of internal multi-hop reasoning processes in large language models. We show that during inference, the middle layers of the network generate highly interpretable embeddings. Our findings can help uncover the strategies that LLMs use to solve reasoning tasks, offering insights into the types of thought processes that can emerge from artificial intelligence.
  arXiv  Detail & Related papers  (2024-06-19T21:36:40Z)
• An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs [8.861378619584093]
  Large language models (LLMs) have shown strong arithmetic reasoning capabilities when prompted with Chain-of-Thought prompts. We investigate neuron activation'' as a lens to provide a unified explanation to observations made by prior work.
  arXiv  Detail & Related papers  (2024-06-18T05:49:24Z)
• Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs [52.42505579545893]
  Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought explanations alongside answers. We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs' knowledge of reasoning and the accuracy of the generated CoT.
  arXiv  Detail & Related papers  (2024-02-17T05:22:56Z)
• Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models [107.07851578154242]
  Language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities. It is unclear whether LMs perform tasks by cheating with answers memorized from pretraining corpus, or, via a multi-step reasoning mechanism. We show that MechanisticProbe is able to detect the information of the reasoning tree from the model's attentions for most examples.
  arXiv  Detail & Related papers  (2023-10-23T01:47:29Z)
• 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.