Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
Recent advancements in large language models (LLMs) have shown strong general
reasoning abilities, yet their effectiveness in financial reasoning remains
underexplored. In this study, we comprehensively evaluate 16 powerful reasoning
and general LLMs on three complex financial tasks involving financial text,
tabular data, and equations, assessing numerical reasoning, tabular
interpretation, financial terminology comprehension, long-context processing,
and equation-based problem solving. Our results show that while better datasets
and pretraining improve financial reasoning, general enhancements like CoT
fine-tuning do not always yield consistent gains. Moreover, all reasoning
strategies face challenges in improving performance on long-context and
multi-table tasks. To address these limitations, we develop a financial
reasoning-enhanced model based on Llama-3.1-8B-Instruct, by CoT fine-tuning and
reinforcement learning with domain-specific reasoning paths. Even with simple
fine-tuning with one financial dataset, our model achieves a consistent 10%
performance improvement across tasks, surpassing all 8B models and even
Llama3-70B-Instruct and Llama3.1-70B-Instruct on average. Our results highlight
the need for domain-specific adaptations in financial tasks, emphasizing future
directions such as multi-table reasoning, long-context processing, and
financial terminology comprehension. All our datasets, models, and codes are
publicly available. Furthermore, we introduce a leaderboard for benchmarking
future datasets and models.
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Lingfei Qian
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2025-06-11T20:21:33.656Z
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Lingfei Qian
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Weipeng Zhou
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Yan Wang
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2025-05-28T19:15:18.741Z
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Yan Wang
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Xueqing Peng
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2025-06-03T09:14:22.042Z
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Xueqing Peng
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Xueqing
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Jimin Huang
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2025-02-19T03:28:40.861Z
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Jimin Huang
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Qianqian Xie
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2025-02-13T08:22:01.539Z
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Xie
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QianqianXie1994
Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
Recent advancements in large language models (LLMs) have shown strong general
reasoning abilities, yet their effectiveness in financial reasoning remains
underexplored. In this study, we comprehensively evaluate 16 powerful reasoning
and general LLMs on three complex financial tasks involving financial text,
tabular data, and equations, assessing numerical reasoning, tabular
interpretation, financial terminology comprehension, long-context processing,
and equation-based problem solving. Our results show that while better datasets
and pretraining improve financial reasoning, general enhancements like CoT
fine-tuning do not always yield consistent gains. Moreover, all reasoning
strategies face challenges in improving performance on long-context and
multi-table tasks. To address these limitations, we develop a financial
reasoning-enhanced model based on Llama-3.1-8B-Instruct, by CoT fine-tuning and
reinforcement learning with domain-specific reasoning paths. Even with simple
fine-tuning with one financial dataset, our model achieves a consistent 10%
performance improvement across tasks, surpassing all 8B models and even
Llama3-70B-Instruct and Llama3.1-70B-Instruct on average. Our results highlight
the need for domain-specific adaptations in financial tasks, emphasizing future
directions such as multi-table reasoning, long-context processing, and
financial terminology comprehension. All our datasets, models, and codes are
publicly available. Furthermore, we introduce a leaderboard for benchmarking
future datasets and models.
A study evaluates 16 large language models on complex financial tasks, finding that domain-specific CoT fine-tuning and reinforcement learning improve performance and highlight the need for further research on long-context and multi-table reasoning.
large language models financial reasoning CoT fine-tuning reinforcement learning domain-specific reasoning paths multi-table reasoning long-context processing