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arXiv · 2504.13125

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard

Varun Rao·Youran Sun·Mahendra Kumar·Tejas Mutneja·Agastya Mukherjee·Haizhao Yang
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Abstract

This paper investigates the application of large language models (LLMs) to financial tasks. We fine-tuned foundation models using the Open FinLLM Leaderboard as a benchmark. Building on Qwen2.5 and Deepseek-R1, we employed techniques including supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL) to enhance their financial capabilities. The fine-tuned models demonstrated substantial performance gains across a wide range of financial tasks. Moreover, we measured the data scaling law in the financial domain. Our work demonstrates the potential of large language models (LLMs) in financial applications.

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Varun Rao, Youran Sun, Mahendra Kumar, Tejas Mutneja, Agastya Mukherjee, Haizhao Yang. 2025-04-17. LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard. https://arxiv.org/abs/2504.13125

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