用大模型当路由，让专家系统更懂股市上下文。
LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading
- 用大模型替代传统神经网络做专家选择路由
- 在真实多模态数据上超越现有MoE模型表现
- 适合需要可解释性与动态决策的量化交易场景
深度学习和大语言模型（LLMs）的发展推动了混合专家（MoE）机制在股票投资领域的应用。尽管这些模型展现出良好的交易性能，但通常为单模态，忽视文本等其他信息源。传统神经网络路由机制未能考虑上下文与现实细节，导致专家选择不优。为此，我们提出LLMoE框架，将大模型作为MoE中的路由组件，利用其丰富的世界知识与推理能力，基于历史价格数据与股票新闻选择专家。该方法提供更有效且可解释的专家选择机制。在多模态真实股票数据集上的实验表明，LLMoE优于当前最优的MoE模型及其他深度神经网络方法。此外，其灵活架构便于适配多种下游任务。
原文摘要 · Abstract (English)
Recent advances in deep learning and large language models (LLMs) have facilitated the deployment of the mixture-of-experts (MoE) mechanism in the stock investment domain. While these models have demonstrated promising trading performance, they are often unimodal, neglecting the wealth of information available in other modalities, such as textual data. Moreover, the traditional neural network-based router selection mechanism fails to consider contextual and real-world nuances, resulting in suboptimal expert selection. To address these limitations, we propose LLMoE, a novel framework that employs LLMs as the router within the MoE architecture. Specifically, we replace the conventional neural network-based router with LLMs, leveraging their extensive world knowledge and reasoning capabilities to select experts based on historical price data and stock news. This approach provides a more effective and interpretable selection mechanism. Our experiments on multimodal real-world stock datasets demonstrate that LLMoE outperforms state-of-the-art MoE models and other deep neural network approaches. Additionally, the flexible architecture of LLMoE allows for easy adaptation to various downstream tasks.
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