让AI代理竞争激发创意，提升新闻驱动的股市预测能力。
Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?
- 引入竞争机制，刺激大模型代理生成更创新的预测思路。
- 结合微调小模型在反思阶段识别误导信息，提升判断力。
- 实验证明适度竞争可显著提高预测准确率，适合金融预测研究者。
基于多代理的新闻驱动时间序列预测被视为大语言模型（LLMs）时代的一种潜在范式转变。该任务的核心挑战在于量化不同新闻事件对时间序列波动的影响，要求代理具备更强的创新思维与识别误导逻辑的能力。然而，现有多代理讨论框架在优化这两项能力方面效果有限。受竞争促进创新的启发，本文在多代理讨论中嵌入竞争机制，以增强代理生成创新思想的能力。为进一步提升模型识别误导信息的能力，我们在反思阶段引入一个微调的小规模LLM模型，提供辅助决策支持。实验结果表明，竞争能有效提升代理的创新思维能力，显著改善时间序列预测性能。类似社会科学发现，该框架内竞争强度会影响代理表现，为基于LLMs的多代理系统研究提供了新视角。
原文摘要 · Abstract (English)
Multi-agents-based news-driven time series forecasting is considered as a potential paradigm shift in the era of large language models (LLMs). The challenge of this task lies in measuring the influences of different news events towards the fluctuations of time series. This requires agents to possess stronger abilities of innovative thinking and the identifying misleading logic. However, the existing multi-agent discussion framework has limited enhancement on time series prediction in terms of optimizing these two capabilities. Inspired by the role of competition in fostering innovation, this study embeds a competition mechanism within the multi-agent discussion to enhance agents' capability of generating innovative thoughts. Furthermore, to bolster the model's proficiency in identifying misleading information, we incorporate a fine-tuned small-scale LLM model within the reflective stage, offering auxiliary decision-making support. Experimental results confirm that the competition can boost agents' capacity for innovative thinking, which can significantly improve the performances of time series prediction. Similar to the findings of social science, the intensity of competition within this framework can influence the performances of agents, providing a new perspective for studying LLMs-based multi-agent systems.
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