Prompt-Based Monte Carlo Tree Search for Mitigating Hallucinations in Large Models
This work addresses hallucinations in large models for scientific research applications, but it appears incremental as it builds on existing MCTS methods with adaptive adjustments.
The study tackled the problem of hallucinations in large models when handling complex scientific problems by proposing a prompt-based improved Monte Carlo Tree Search method, which showed better performance on the SciEval dataset compared to existing models.
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. The results show that the Improved MCTS method performs better, providing new ideas and methods for the application of large models in the field of scientific research.