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

TL;DR

This paper introduces a prompt-based Monte Carlo Tree Search method that dynamically adjusts exploration parameters to reduce hallucinations in large models, improving their reliability in scientific research applications.

Contribution

It presents an improved MCTS approach with adaptive strategies and demonstrates its effectiveness over existing models in scientific datasets.

Findings

Reduced hallucination phenomena in large models.

Enhanced performance on SciEval dataset subsets.

Provides new methods for applying large models in scientific research.

Abstract

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.