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

• URL: http://arxiv.org/abs/2504.13125v1
• Date: Thu, 17 Apr 2025 17:42:02 GMT
• Title: LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard
• Authors: Varun Rao, Youran Sun, Mahendra Kumar, Tejas Mutneja, Agastya Mukherjee, Haizhao Yang,
• Abstract summary: We fine-tuned foundation models using the Open FinLLM Leaderboard as a benchmark.<n>We employed techniques including supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL) to enhance their financial capabilities.
• Score: 4.629032441868537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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