LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917988827922432 |
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| author | Rao, Varun Sun, Youran Kumar, Mahendra Mutneja, Tejas Mukherjee, Agastya Yang, Haizhao |
| author_facet | Rao, Varun Sun, Youran Kumar, Mahendra Mutneja, Tejas Mukherjee, Agastya Yang, Haizhao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Rao, Varun Sun, Youran Kumar, Mahendra Mutneja, Tejas Mukherjee, Agastya Yang, Haizhao Computation and Language Artificial Intelligence Machine Learning 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. |
| title | LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.13125 |