CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning

• URL: http://arxiv.org/abs/2502.02390v1
• Date: Tue, 04 Feb 2025 15:10:33 GMT
• Title: CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning
• Authors: Jianfeng Pan, Senyou Deng, Shaomang Huang,
• Abstract summary: Chain-of-Associated-Thoughts (CoAT) framework introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed 'associative memory' By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. These experiments demonstrated that our framework outperforms conventional inference processes on accuracy, coherence, and diversity.
• Score: 0.8192907805418583
• License:
• Abstract: Research on LLM technologies is rapidly emerging, with most of them employing a 'fast thinking' approach to inference. Most LLMs generate the final result based solely on a single query and LLM's reasoning capabilities. However, with the advent of OpenAI-o1, 'slow thinking' techniques have garnered increasing attention because its process is closer to the human thought process. Inspired by the human ability to constantly associate and replenish knowledge during thinking, we developed the novel Chain-of-Associated-Thoughts (CoAT) framework, which introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed 'associative memory'. By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. This allows the framework to not only revisit and refine earlier inferences but also adaptively incorporate evolving information, ensuring that the final output is both accurate and comprehensive. To validate the effectiveness of our framework, we conducted extensive experiments across a range of generative and reasoning tasks. These experiments demonstrated that our framework outperforms conventional inference processes on accuracy, coherence, and diversity. The framework's ability to iteratively expand its search space while retaining contextually relevant information results.

Related papers

• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs) We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs. We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• Learning to Generate Research Idea with Dynamic Control [21.30777644522451]
  Large language models (LLMs) have shown promise in generating hypotheses and research ideas. We introduce a novel framework that employs a two-stage approach combiningSupervised Fine-Tuning (SFT) and controllable Reinforcement Learning (RL) Our framework provides a balanced approach to research ideation, achieving high-quality outcomes by dynamically navigating the trade-offs among novelty, feasibility, and effectiveness.
  arXiv  Detail & Related papers  (2024-12-19T08:28:18Z)
• LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research [5.564972490390789]
  We present a framework that explicitly implements creativity theory using Large Language Models (LLMs) The framework features a generalization-level retrieval system for cross-domain knowledge discovery and a structured process for idea generation. Experiments on the OAG-Bench dataset demonstrate our framework's effectiveness, consistently outperforming baseline approaches in generating ideas that align with real research developments.
  arXiv  Detail & Related papers  (2024-12-18T18:41:14Z)
• Enhancing LLM Reasoning with Reward-guided Tree Search [95.06503095273395]
  o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research. We present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms.
  arXiv  Detail & Related papers  (2024-11-18T16:15:17Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• A Unified and General Framework for Continual Learning [58.72671755989431]
  Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies.
  arXiv  Detail & Related papers  (2024-03-20T02:21:44Z)
• Demystifying Chains, Trees, and Graphs of Thoughts [20.980650840083385]
  We focus on identifying fundamental classes of harnessed structures, and we analyze the representations of these structures. Our study compares existing prompting schemes using the proposed taxonomy, discussing how certain design choices lead to different patterns in performance and cost.
  arXiv  Detail & Related papers  (2024-01-25T16:34:00Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond [114.39616146985001]
  In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of Bi-Level Optimization (BLO) Then we construct a value-function-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies.
  arXiv  Detail & Related papers  (2021-01-27T16:20:23Z)

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.