Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs

• URL: http://arxiv.org/abs/2502.10858v2
• Date: Tue, 18 Feb 2025 07:58:19 GMT
• Title: Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
• Authors: Zongqian Wu, Tianyu Li, Baoduo Xu, Jiaying Yang, Mengmeng Zhan, Xiaofeng Zhu, Lei Feng,
• Abstract summary: We investigate whether relevant knowledge that contributes directly to solving the given question can be activated from the initial reasoning path. Our experiments reveal that increasing the diversity of initial reasoning paths can achieve comparable or superior performance. We propose a simple yet effective method that enhances reasoning breadth by integrating contextual exploration with reduced sampling randomness.
• Score: 11.896234713853298
• License:
• Abstract: Deep iterative chain-of-thought (CoT) reasoning enables LLMs to tackle complex tasks by progressively activating relevant pre-trained knowledge. However, it faces challenges in ensuring continual improvement and determining a stopping criterion. In this paper, we investigate whether the relevant knowledge that contributes directly to solving the given question can be activated from the initial reasoning path, thus circumventing the need for iterative refinement. Our experiments reveal that increasing the diversity of initial reasoning paths can achieve comparable or superior performance, a concept we term \textit{breadth reasoning}. However, existing breadth reasoning approaches, such as self-consistency, offer limited diversity. To address this limitation, we propose a simple yet effective method that enhances reasoning breadth by integrating contextual exploration with reduced sampling randomness. Extensive experiments demonstrate that our approach significantly outperforms deep iterative reasoning. Our code is provided in https://github.com/zongqianwu/breadth.

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)
• 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)
• 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)
• Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension [9.67774998354062]
  Previous research has primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. We propose a Premise-Oriented Data Augmentation (PODA) framework to generate CoT rationales including analyses for both correct and incorrect options. We also introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples.
  arXiv  Detail & Related papers  (2024-09-22T15:44:43Z)
• Chain-of-Thought Reasoning Without Prompting [40.92854235219315]
  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.
  arXiv  Detail & Related papers  (2024-02-15T18:55:41Z)
• 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)
• LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning [61.7853049843921]
  Chain-of-thought (CoT) prompting is a popular in-context learning approach for large language models (LLMs) This paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales.
  arXiv  Detail & Related papers  (2023-12-07T20:36:10Z)
• Implicit Chain of Thought Reasoning via Knowledge Distillation [58.80851216530288]
  Instead of explicitly producing the chain of thought reasoning steps, we use the language model's internal hidden states to perform implicit reasoning. We find that this approach enables solving tasks previously not solvable without explicit chain-of-thought, at a speed comparable to no chain-of-thought.
  arXiv  Detail & Related papers  (2023-11-02T17:59:49Z)
• 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)

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.