Prompt-Based Monte Carlo Tree Search for Mitigating Hallucinations in Large Models

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With the rapid development of large models in the field of artificial intelligence, how to enhance their application capabilities in handling complex problems in the field of scientific research remains a challenging problem to be solved.

• This study proposes an improved Monte Carlo Tree Search (MCTS) method based on prompt words.
• In the simulation search stage, it introduces dynamic adjustment of exploration parameters and adaptive selection strategies, which can better balance exploration and exploitation, thereby reducing the hallucination phenomenon.
• This paper takes the four subsets of the SciEval dataset as the test objects, and compares the Glm-4-flash+Improved MCTS method with the methods of several existing models.