FinTeamExperts: Role Specialized MOEs For Financial Analysis

TL;DR

FinTeamExperts introduces a role-specialized mixture of experts framework with three 8-billion parameter LLMs, each focusing on macro, micro, or quantitative financial analysis, leading to improved performance on diverse financial tasks.

Contribution

The paper presents a novel role-specific MOE framework for financial analysis, training specialized models for macro, micro, and quantitative roles, and demonstrates superior performance over comparable models.

Findings

Outperforms same-size models on three of four datasets

Maintains superior performance on complex tasks

Role specialization enhances domain expertise integration

Abstract

Large Language Models (LLMs), such as ChatGPT, Phi3 and Llama-3, are leading a significant leap in AI, as they can generalize knowledge from their training to new tasks without fine-tuning. However, their application in the financial domain remains relatively limited. The financial field is inherently complex, requiring a deep understanding across various perspectives, from macro, micro economic trend to quantitative analysis. Motivated by this complexity, a mixture of expert LLMs tailored to specific financial domains could offer a more comprehensive understanding for intricate financial tasks. In this paper, we present the FinTeamExperts, a role-specialized LLM framework structured as a Mixture of Experts (MOEs) for financial analysis. The framework simulates a collaborative team setting by training each model to specialize in distinct roles: Macro Analysts, Micro analysts, and Quantitative Analysts. This role-specific specialization enhances the model's ability to integrate their domain-specific expertise. We achieve this by training three 8-billion parameter models on different corpus, each dedicated to excelling in specific finance-related roles. We then instruct-tune FinTeamExperts on downstream tasks to align with practical financial tasks. The experimental results show that FinTeamExperts outperform all models of the same size and larger on three out of four datasets. On the fourth dataset, which presents a more complex task, FinTeamExperts still surpass all models of the same size. This highlights the success of our role-based specialization approach and the continued training approach for FinTeamExperts.