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    <abstract>We introduce KnowledgeFMath, a novel benchmark designed to evaluate LLMs’ capabilities in solving knowledge-intensive math reasoning problems. Compared to prior works, this study features three core advancements. First, KnowledgeFMath includes 1,259 problems with a hybrid of textual and tabular content. These problems require college-level knowledge in the finance domain for effective resolution. Second, we provide expert-annotated, detailed solution references in Python program format, ensuring a high-quality benchmark for LLM assessment. We also construct a finance-domain knowledge bank and investigate various knowledge integration strategies. Finally, we evaluate a wide spectrum of 26 LLMs with different prompting strategies like Chain-of-Thought and Program-of-Thought. Our experimental results reveal that the current best-performing system (i.e., GPT-4 with CoT prompting) achieves only 56.6% accuracy, leaving substantial room for improvement. Moreover, while augmenting LLMs with external knowledge can improve their performance (e.g., from 33.5% to 47.1% for GPT-3.5), their accuracy remains significantly lower than the estimated human expert performance of 92%. We believe that KnowledgeFMath can advance future research in the area of domain-specific knowledge retrieval and integration, particularly within the context of solving math reasoning problems.</abstract>
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%0 Conference Proceedings
%T KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains
%A Zhao, Yilun
%A Liu, Hongjun
%A Long, Yitao
%A Zhang, Rui
%A Zhao, Chen
%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-knowledgefmath
%X We introduce KnowledgeFMath, a novel benchmark designed to evaluate LLMs’ capabilities in solving knowledge-intensive math reasoning problems. Compared to prior works, this study features three core advancements. First, KnowledgeFMath includes 1,259 problems with a hybrid of textual and tabular content. These problems require college-level knowledge in the finance domain for effective resolution. Second, we provide expert-annotated, detailed solution references in Python program format, ensuring a high-quality benchmark for LLM assessment. We also construct a finance-domain knowledge bank and investigate various knowledge integration strategies. Finally, we evaluate a wide spectrum of 26 LLMs with different prompting strategies like Chain-of-Thought and Program-of-Thought. Our experimental results reveal that the current best-performing system (i.e., GPT-4 with CoT prompting) achieves only 56.6% accuracy, leaving substantial room for improvement. Moreover, while augmenting LLMs with external knowledge can improve their performance (e.g., from 33.5% to 47.1% for GPT-3.5), their accuracy remains significantly lower than the estimated human expert performance of 92%. We believe that KnowledgeFMath can advance future research in the area of domain-specific knowledge retrieval and integration, particularly within the context of solving math reasoning problems.
%U https://aclanthology.org/2024.acl-long.693
%P 12841-12858

Markdown (Informal)

[KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains](https://aclanthology.org/2024.acl-long.693) (Zhao et al., ACL 2024)

• KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains (Zhao et al., ACL 2024)

ACL

• Yilun Zhao, Hongjun Liu, Yitao Long, Rui Zhang, Chen Zhao, and Arman Cohan. 2024. KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12841–12858, Bangkok, Thailand. Association for Computational Linguistics.