Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?
Yuxuan Zhang, Yangyang Feng, Daifeng Li, Kexin Zhang, Junlan Chen, Bowen Deng·April 14, 2025
Summary
A study improves multi-agent forecasting by introducing a competition mechanism, optimizing large language model-based systems. This enhances agents' analytical and judgment skills, surpassing baseline models. Evaluations focus on logic quality, assessing performance improvements. Strategies boost performance by removing deceptive logic, emphasizing controllability and trend analysis. Experiments on LLMs show promise, with a sensitivity analysis on the retention ratio's effect on model performance. The study also covers refining news filtering logic through self-examination. Three Machine Learning creativity challenges ranked {rank} out of {total} participants, assessing top score ({top_value}), average score ({ave_value}), and combined effectiveness ({top_value}, {average_value}).
Introduction
Background
Overview of multi-agent forecasting systems
Importance of analytical and judgment skills in forecasting
Current limitations of baseline models in multi-agent systems
Objective
Aim of the study: Enhancing multi-agent forecasting through a competition mechanism
Focus on optimizing large language model-based systems
Objective to surpass baseline models in analytical and judgment skills
Method
Data Collection
Techniques for gathering data for multi-agent forecasting
Importance of diverse and relevant data in improving model performance
Data Preprocessing
Methods for cleaning and preparing data for model training
Role of preprocessing in enhancing the effectiveness of large language models
Competition Mechanism
Design and implementation of the competition mechanism
How the mechanism encourages agents to improve analytical and judgment skills
Large Language Model Optimization
Techniques for optimizing large language models for multi-agent forecasting
Focus on enhancing logic quality and performance improvements
Evaluation
Logic Quality Assessment
Metrics for evaluating the quality of logic in multi-agent systems
Importance of logic quality in forecasting accuracy
Performance Improvements
Strategies for boosting performance by removing deceptive logic
Emphasis on controllability and trend analysis in enhancing model performance
Experiments and Results
LLMs Performance
Detailed results of experiments on large language models
Analysis of performance improvements and challenges
Sensitivity Analysis
Examination of the impact of the retention ratio on model performance
Insights into optimizing the balance between model complexity and performance
News Filtering Logic Refinement
Self-Examination Techniques
Methods for refining news filtering logic through self-examination
Role of self-examination in improving the relevance and accuracy of news filtering
Machine Learning Creativity Challenges
Challenge Overview
Description of the three Machine Learning creativity challenges
Context and criteria for ranking participants
Performance Metrics
Assessment of top score, average score, and combined effectiveness
Detailed analysis of the study's ranking and performance metrics
Conclusion
Summary of Findings
Recap of the study's main achievements and improvements
Implications
Discussion of the study's implications for multi-agent forecasting systems
Potential for future research and applications
Recommendations
Suggestions for further enhancing multi-agent forecasting systems
Areas for improvement and future development
Basic info
papers
artificial intelligence
Advanced features
Insights
What strategies are employed to enhance the logic quality and controllability of large language models?
What is the impact of the retention ratio on the performance of large language models in the study?
How does the competition mechanism improve the performance of multi-agent forecasting systems?
How does the study refine news filtering logic through self-examination?