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Knowledge-Intensive Math Reasoning in Finance Domains

1Yale University, 2NYU Shanghai, 3New York University
4Penn State University, 5Allen Institute for AI
ACL 2024 Oral
data-overview

Overview of FinanceMath.

Introduction

Large Language Models (LLMs) have demonstrated impressive problem-solving capabilities across various domains, but their ability to handle knowledge-intensive mathematical reasoning in specialized fields remains understudied. To address this gap, we introduce FinanceMath, a benchmark designed to evaluate LLMs' performance in knowledge-intensive mathematical reasoning within the finance domain. FinanceMath comprises 1200 examples covering a wide range of finance subareas, with 40.2% of problems requiring interpretation of tabular data. Each problem is accompanied by expert-annotated, Python-formatted solutions.

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FinanceMath Dataset

Visualization

BibTeX

@misc{zhao2024financemath,
      title={FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains}, 
      author={Yilun Zhao and Hongjun Liu and Yitao Long and Rui Zhang and Chen Zhao and Arman Cohan},
      year={2024},
      eprint={2311.09797},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2311.09797}, 
}