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

ACL ARR 2025 May Submission87 Authors

Abstract: Research on LLM technologies is rapidly emerging, with most of them employ 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 CoAT outperforms conventional inference processes on accuracy, coherence, and diversity.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: retrieval-augmented models, continual learning

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings

Languages Studied: English

Keywords: LLM, Reasoning Enhancement, Associative Memory, Optimized MCTS

Submission Number: 87