@inproceedings{zhao-etal-2024-docmath,
title = "{D}oc{M}ath-Eval: Evaluating Math Reasoning Capabilities of {LLM}s in Understanding Financial Documents",
author = "Zhao, Yilun and
Long, Yitao and
Liu, Hongjun and
Kamoi, Ryo and
Nan, Linyong and
Chen, Lyuhao and
Liu, Yixin and
Tang, Xiangru and
Zhang, Rui and
Cohan, Arman",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.852",
pages = "16103--16120",
abstract = "Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexplored. This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning capabilities of LLMs in the context of understanding and analyzing financial documents containing both text and tables. We evaluate a wide spectrum of 27 LLMs, including those specialized in math, coding and finance, with Chain-of-Thought and Program-of-Thought prompting methods. We found that even the current best-performing system (i.e., GPT-4) still significantly lags behind human experts in solving complex numerical reasoning problems grounded in long contexts. We believe DocMath-Eval can be used as a valuable benchmark to evaluate LLMs{'} capabilities to solve challenging numerical reasoning problems in expert domains.",
}
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<abstract>Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexplored. This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning capabilities of LLMs in the context of understanding and analyzing financial documents containing both text and tables. We evaluate a wide spectrum of 27 LLMs, including those specialized in math, coding and finance, with Chain-of-Thought and Program-of-Thought prompting methods. We found that even the current best-performing system (i.e., GPT-4) still significantly lags behind human experts in solving complex numerical reasoning problems grounded in long contexts. We believe DocMath-Eval can be used as a valuable benchmark to evaluate LLMs’ capabilities to solve challenging numerical reasoning problems in expert domains.</abstract>
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%0 Conference Proceedings
%T DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Financial Documents
%A Zhao, Yilun
%A Long, Yitao
%A Liu, Hongjun
%A Kamoi, Ryo
%A Nan, Linyong
%A Chen, Lyuhao
%A Liu, Yixin
%A Tang, Xiangru
%A Zhang, Rui
%A Cohan, Arman
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhao-etal-2024-docmath
%X Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexplored. This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning capabilities of LLMs in the context of understanding and analyzing financial documents containing both text and tables. We evaluate a wide spectrum of 27 LLMs, including those specialized in math, coding and finance, with Chain-of-Thought and Program-of-Thought prompting methods. We found that even the current best-performing system (i.e., GPT-4) still significantly lags behind human experts in solving complex numerical reasoning problems grounded in long contexts. We believe DocMath-Eval can be used as a valuable benchmark to evaluate LLMs’ capabilities to solve challenging numerical reasoning problems in expert domains.
%U https://aclanthology.org/2024.acl-long.852
%P 16103-16120
Markdown (Informal)
[DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Financial Documents](https://aclanthology.org/2024.acl-long.852) (Zhao et al., ACL 2024)
ACL
- Yilun Zhao, Yitao Long, Hongjun Liu, Ryo Kamoi, Linyong Nan, Lyuhao Chen, Yixin Liu, Xiangru Tang, Rui Zhang, and Arman Cohan. 2024. DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Financial Documents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16103–16120, Bangkok, Thailand. Association for Computational Linguistics.