title: Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
summary: 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.
paper_authors._id: 67ad5ca29109885ce9b859e4
paper_authors.name: Lingfei Qian
paper_authors._id: 67ad5ca29109885ce9b859e5
paper_authors.name: Weipeng Zhou
paper_authors._id: 67ad5ca29109885ce9b859e6
paper_authors.name: Yan Wang
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paper_authors.fullname: Yan Wang
paper_authors.type: user
paper_authors.user: YanAdjeNole
paper_authors._id: 67ad5ca29109885ce9b859e7
paper_authors.name: Xueqing Peng
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paper_authors.fullname: Xueqing Peng
paper_authors.type: user
paper_authors.user: Xueqing
paper_authors._id: 67ad5ca29109885ce9b859e8
paper_authors.name: Jimin Huang
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paper_authors.fullname: Jimin Huang
paper_authors.type: user
paper_authors.user: jiminHuang
paper_authors._id: 67ad5ca29109885ce9b859e9
paper_authors.name: Qianqian Xie
paper_authors.status: claimed_verified
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paper_authors._id: 6479f4317c18dca75e9a9324
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paper_authors.fullname: Xie
paper_authors.type: user
paper_authors.user: QianqianXie1994
paper_title: Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: large language models financial reasoning CoT fine-tuning reinforcement learning domain-specific reasoning paths multi-table reasoning long-context processing