LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading
This addresses the need for more effective and interpretable trading models in finance, though it appears incremental by adapting existing LLM capabilities to a specific domain.
The authors tackled the problem of suboptimal expert selection in mixture-of-experts models for stock trading by proposing LLMoE, a framework that uses large language models as routers, which outperformed state-of-the-art models on real-world datasets.
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.