Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture
This work addresses the efficiency problem for financial trading applications by enabling faster inference through distillation, though it is incremental as it builds on existing LLM and distillation techniques.
The paper tackles the slow inference speed of large language models (LLMs) in market making by proposing a distillation framework that decomposes LLM features across layer, task, and data dimensions, achieving superior performance over existing methods on four real-world datasets.
Market making (MM) through Reinforcement Learning (RL) has attracted significant attention in financial trading. With the development of Large Language Models (LLMs), more and more attempts are being made to apply LLMs to financial areas. A simple, direct application of LLM as an agent shows significant performance. Such methods are hindered by their slow inference speed, while most of the current research has not studied LLM distillation for this specific task. To address this, we first propose the normalized fluorescent probe to study the mechanism of the LLM's feature. Based on the observation found by our investigation, we propose Cooperative Market Making (CMM), a novel framework that decouples LLM features across three orthogonal dimensions: layer, task, and data. Various student models collaboratively learn simple LLM features along with different dimensions, with each model responsible for a distinct feature to achieve knowledge distillation. Furthermore, CMM introduces an Hájek-MoE to integrate the output of the student models by investigating the contribution of different models in a kernel function-generated common feature space. Extensive experimental results on four real-world market datasets demonstrate the superiority of CMM over the current distillation method and RL-based market-making strategies.