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'<n>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.<n>These experiments demonstrated that our framework outperforms conventional inference processes on accuracy, coherence, and diversity.
• Score: 0.8192907805418583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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