arXiv:2501.13942v1 Announce Type: new
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.
Enhancing the Application Capabilities of Large AI Models in Scientific Research with Improved MCTS Method
The rapid development of large models in the field of artificial intelligence has significantly advanced the capabilities of machine learning algorithms. However, applying these models effectively to handle complex problems in scientific research remains a challenging task. This study addresses this problem by proposing an improved Monte Carlo Tree Search (MCTS) method based on prompt words.
The MCTS algorithm is commonly used in decision-making processes and game playing scenarios. It combines the exploration of possible moves with the exploitation of previously gained knowledge to make informed decisions. However, applying MCTS to large AI models for scientific research comes with its own set of challenges, such as hallucination phenomena where the model generates unrealistic results.
The proposed Improved MCTS method introduces dynamic adjustment of exploration parameters and adaptive selection strategies during the simulation search stage. This allows for a better balance between exploration and exploitation, reducing the chances of hallucination and improving the overall performance of the large AI models in scientific research.
To evaluate the effectiveness of the Improved MCTS method, the study conducted experiments using the SciEval dataset. The dataset consists of four subsets, providing a diverse set of scientific research scenarios. A comparative analysis was performed against several existing models, including the Glm-4-flash method. The results demonstrated that the Improved MCTS method outperformed the other models, showcasing its potential for enhancing the application of large models in the field of scientific research.
Multi-disciplinary Nature of the Concepts
This research study highlights the multi-disciplinary nature of the concepts discussed. The combination of artificial intelligence, large models, Monte Carlo Tree Search algorithm, and scientific research encompasses different fields such as computer science, statistics, game theory, and domain-specific scientific knowledge.
The introduction of prompt words and the dynamic adjustment of exploration parameters require a deep understanding of natural language processing techniques and AI-based decision-making algorithms. Additionally, the evaluation of performance against existing models demonstrates the need for statistical analysis and domain-specific knowledge to interpret and compare the results accurately.
Overall, the application of large AI models in scientific research requires expertise from various disciplines and the integration of techniques and concepts from those fields to address the unique challenges of the domain.
Expert Insight: The Improved MCTS method proposed in this study provides valuable insights into advancing the application capabilities of large AI models in scientific research. This research serves as a stepping stone to improving decision-making processes and exploring complex problems within scientific domains. However, further research is needed to validate the method’s performance across various scientific disciplines and datasets. Additionally, incorporating other techniques like reinforcement learning or attention mechanisms could potentially enhance the capabilities of large AI models even further.