Improving LLM Accuracy with Monte Carlo Tree Search

In this video, Trelis Research explores how to improve the accuracy of large language models (LLMs) using Monte Carlo Tree Search (MCTS). The technique aims to reduce hallucinations and enhance the performance of models like Llama 3 8B, potentially bringing them up to the level of GPT-4 Turbo on certain benchmarks.

The video begins by showcasing results from a recent paper titled ‘Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B.’ The paper demonstrates significant performance improvements on grade school exams and math odyssey tests using MCTS, with Llama 3 8B achieving scores comparable to or even exceeding GPT-4 Turbo.

The core concept behind MCTS is explained, highlighting how it systematically varies prompts and refines answers programmatically. This approach contrasts with manual prompt optimization, providing a more efficient way to explore and exploit potential solutions. The process involves generating seed answers, iterating on those answers, and scoring them to guide further exploration.

A simple example of the question ‘What is 2 + 2 + 2?’ is used to illustrate how MCTS works. The technique balances exploitation (improving good answers) and exploration (trying new answers) using the Upper Confidence Bound (UCT) formula. This formula helps decide which nodes to expand based on their scores and visit counts.

The video then transitions to a Jupyter Notebook demonstration, where the presenter walks through the implementation of MCTS to solve math problems. The notebook includes functions for generating critiques, improving answers, and rating responses. The process is tested on a simple math problem, showing how MCTS can improve the accuracy of the answers.

The limitations of MCTS are also discussed, including its computational cost and the need for many iterations, which can make it slow and expensive. The technique is best suited for high-quality, low-latency tasks rather than real-time applications.

Overall, the video provides a comprehensive guide to using Monte Carlo Tree Search to enhance the performance of large language models, offering valuable insights and practical steps for implementation.