Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance

• URL: http://arxiv.org/abs/2502.08127v2
• Date: Fri, 28 Mar 2025 08:33:36 GMT
• Title: Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
• Authors: Lingfei Qian, Weipeng Zhou, Yan Wang, Xueqing Peng, Han Yi, Jimin Huang, Qianqian Xie, Jianyun Nie,
• Abstract summary: Large language models (LLMs) have shown strong general reasoning capabilities, but their effectiveness in financial reasoning remains underexplored.<n>We evaluate 24 state-of-the-art general and reasoning-focused LLMs across four complex financial reasoning tasks.<n>We propose two domain-adapted models, Fino1-8B and FinoB, trained with chain-of-thought (CoT) fine-tuning and reinforcement learning.
• Score: 32.516564836540745
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While large language models (LLMs) have shown strong general reasoning capabilities, their effectiveness in financial reasoning, which is crucial for real-world financial applications remains underexplored. In this study, we conduct a comprehensive evaluation of 24 state-of-the-art general and reasoning-focused LLMs across four complex financial reasoning tasks involving financial text, tabular data, and equations. We assess key capabilities such as numerical reasoning, tabular interpretation, financial terminology comprehension, long-context understanding, and equation-based problem solving. Our analysis reveals that while data quality and pretraining contribute to performance, general techniques like chain-of-thought (CoT) fine-tuning offer limited gains in financial tasks. To address this, we propose two domain-adapted models, Fino1-8B and Fino1-14B, trained with CoT fine-tuning and reinforcement learning using domain-specific reasoning paths. Our models are trained on a carefully curated dataset integrating high-quality examples from diverse sources, covering financial reports, tables, equations, and structured XBRL texts. Despite limited training data, they achieve an 7-9% performance improvement, outperforming several advanced LLMs, including GPT-o1, GPT-o3-mini, GPT-4.5, and comparable with DeepSeek models (V3 and R1), demonstrating strong practical value in resource, constrained scenarios. Our findings highlight the need for domain-specific adaptations in financial reasoning, and we release all datasets, models, and code for future research.

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