@inproceedings{tang-etal-2025-financereasoning,
title = "{F}inance{R}easoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging",
author = "Tang, Zichen and
E, Haihong and
Ma, Ziyan and
He, Haoyang and
Liu, Jiacheng and
Yang, Zhongjun and
Rong, Zihua and
Li, Rongjin and
Ji, Kun and
Huang, Qing and
Hu, Xinyang and
Liu, Yang and
Zheng, Qianhe",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.766/",
doi = "10.18653/v1/2025.acl-long.766",
pages = "15721--15749",
ISBN = "979-8-89176-251-0",
abstract = "We introduce **FinanceReasoning**, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compared to existing benchmarks, our work provides three key advancements. (1) **Credibility**: We update 15.6{\%} of the questions from four public datasets, annotating 908 new questions with detailed Python solutions and rigorously refining evaluation standards. This enables an accurate assessment of the reasoning improvements of LRMs. (2) **Comprehensiveness**: FinanceReasoning covers 67.8{\%} of financial concepts and formulas, significantly surpassing existing datasets. Additionally, we construct 3,133 Python-formatted functions, which enhances LRMs' financial reasoning capabilities through refined knowledge (*e.g.*, 83.2{\%} $\rightarrow$ 91.6{\%} for GPT-4o). (3) **Challenge**: Models are required to apply multiple financial formulas for precise numerical reasoning on 238 *Hard* problems. The best-performing model (*i.e.*, OpenAI o1 with PoT) achieves 89.1{\%} accuracy, yet LRMs still face challenges in numerical precision. We demonstrate that combining Reasoner and Programmer models can effectively enhance LRMs' performance (*e.g.*, 83.2{\%} $\rightarrow$ 87.8{\%} for DeepSeek-R1). Our work paves the way for future research on evaluating and improving LRMs in domain-specific complex reasoning tasks."
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<abstract>We introduce **FinanceReasoning**, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compared to existing benchmarks, our work provides three key advancements. (1) **Credibility**: We update 15.6% of the questions from four public datasets, annotating 908 new questions with detailed Python solutions and rigorously refining evaluation standards. This enables an accurate assessment of the reasoning improvements of LRMs. (2) **Comprehensiveness**: FinanceReasoning covers 67.8% of financial concepts and formulas, significantly surpassing existing datasets. Additionally, we construct 3,133 Python-formatted functions, which enhances LRMs’ financial reasoning capabilities through refined knowledge (*e.g.*, 83.2% \rightarrow 91.6% for GPT-4o). (3) **Challenge**: Models are required to apply multiple financial formulas for precise numerical reasoning on 238 *Hard* problems. The best-performing model (*i.e.*, OpenAI o1 with PoT) achieves 89.1% accuracy, yet LRMs still face challenges in numerical precision. We demonstrate that combining Reasoner and Programmer models can effectively enhance LRMs’ performance (*e.g.*, 83.2% \rightarrow 87.8% for DeepSeek-R1). Our work paves the way for future research on evaluating and improving LRMs in domain-specific complex reasoning tasks.</abstract>
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%0 Conference Proceedings
%T FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging
%A Tang, Zichen
%A E, Haihong
%A Ma, Ziyan
%A He, Haoyang
%A Liu, Jiacheng
%A Yang, Zhongjun
%A Rong, Zihua
%A Li, Rongjin
%A Ji, Kun
%A Huang, Qing
%A Hu, Xinyang
%A Liu, Yang
%A Zheng, Qianhe
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F tang-etal-2025-financereasoning
%X We introduce **FinanceReasoning**, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compared to existing benchmarks, our work provides three key advancements. (1) **Credibility**: We update 15.6% of the questions from four public datasets, annotating 908 new questions with detailed Python solutions and rigorously refining evaluation standards. This enables an accurate assessment of the reasoning improvements of LRMs. (2) **Comprehensiveness**: FinanceReasoning covers 67.8% of financial concepts and formulas, significantly surpassing existing datasets. Additionally, we construct 3,133 Python-formatted functions, which enhances LRMs’ financial reasoning capabilities through refined knowledge (*e.g.*, 83.2% \rightarrow 91.6% for GPT-4o). (3) **Challenge**: Models are required to apply multiple financial formulas for precise numerical reasoning on 238 *Hard* problems. The best-performing model (*i.e.*, OpenAI o1 with PoT) achieves 89.1% accuracy, yet LRMs still face challenges in numerical precision. We demonstrate that combining Reasoner and Programmer models can effectively enhance LRMs’ performance (*e.g.*, 83.2% \rightarrow 87.8% for DeepSeek-R1). Our work paves the way for future research on evaluating and improving LRMs in domain-specific complex reasoning tasks.
%R 10.18653/v1/2025.acl-long.766
%U https://aclanthology.org/2025.acl-long.766/
%U https://doi.org/10.18653/v1/2025.acl-long.766
%P 15721-15749
Markdown (Informal)
[FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging](https://aclanthology.org/2025.acl-long.766/) (Tang et al., ACL 2025)
ACL
- Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, and Qianhe Zheng. 2025. FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15721–15749, Vienna, Austria. Association for Computational Linguistics.