BizBench: A Quantitative Reasoning Benchmark for Business and Finance
Michael Krumdick, Rik Koncel-Kedziorski, Viet Lai, Varshini Reddy, Charles Lovering, Chris Tanner
Abstract
Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for evaluating models’ ability to reason about realistic financial problems. BizBench comprises eight quantitative reasoning tasks, focusing on question-answering (QA) over financial data via program synthesis. We include three financially-themed code-generation tasks from newly collected and augmented QA data. Additionally, we isolate the reasoning capabilities required for financial QA: reading comprehension of financial text and tables for extracting intermediate values, and understanding financial concepts and formulas needed to calculate complex solutions. Collectively, these tasks evaluate a model’s financial background knowledge, ability to parse financial documents, and capacity to solve problems with code. We conduct an in-depth evaluation of open-source and commercial LLMs, comparing and contrasting the behavior of code-focused and language-focused models. We demonstrate that the current bottleneck in performance is due to LLMs’ limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain.- Anthology ID:
- 2024.acl-long.452
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8309–8332
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.452
- DOI:
- Cite (ACL):
- Michael Krumdick, Rik Koncel-Kedziorski, Viet Lai, Varshini Reddy, Charles Lovering, and Chris Tanner. 2024. BizBench: A Quantitative Reasoning Benchmark for Business and Finance. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8309–8332, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- BizBench: A Quantitative Reasoning Benchmark for Business and Finance (Krumdick et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.452.pdf