Enhancing Language Models: Slow Thinking with Monte Carlo Tree Search
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Today on 1littlecoder, the team delves into the intriguing world of enhancing large language models with the revolutionary concept of slow thinking. Inspired by the human brain's system one and system two processes, they explore the paper "C8 Code: Chain of Associated Thoughts," which introduces a framework to enable LLMS to engage in deliberate, methodical decision-making. By incorporating Monte Carlo Tree Search (MCTS), the team aims to revolutionize the way LLMS approach problem-solving, mimicking the intricate processes of human thought.
The discussion centers around the framework's ability to dynamically pull in relevant information during reasoning, akin to how humans connect ideas to form coherent conclusions. Through a delicate balance of exploration and exploitation, the model navigates through various reasoning paths, ensuring a comprehensive exploration of solutions while avoiding repetitive or narrow answers. This innovative approach not only promises better accuracy and diverse solution exploration but also introduces adaptability by providing real-time information through associative memories.
Experimental results on datasets like Lang Chain, Hotpot, and Wiki Multi Hotot showcase the framework's effectiveness in generating more comprehensive and accurate responses compared to traditional models. The qualitative output further highlights the model's enhanced performance when utilizing the Chain of Associated Thoughts framework, underlining the potential for further advancements in this exciting field. With a focus on refining the model's internal reasoning processes and leveraging associative memories, the team sets the stage for a new era in large language model development, sparking curiosity and anticipation for future innovations in this space.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Mention of GraphRAG with MCTS and use of tools in training models
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