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We introduce FinanceMath, a novel benchmark designed to evaluate LLMs'capabilities in solving knowledge-intensive math reasoning problems. Comparedto prior works, this study features three core advancements. First, FinanceMathincludes 1,200 problems with a hybrid of textual and tabular content. Theseproblems require college-level knowledge in the finance domain for effectiveresolution. Second, we provide expert-annotated, detailed solution referencesin Python program format, ensuring a high-quality benchmark for LLM assessment.We also construct a finance-domain knowledge bank and investigate variousknowledge integration strategies. Finally, we evaluate a wide spectrum of 44LLMs with both Chain-of-Thought and Program-of-Thought prompting methods. Ourexperimental results reveal that the current best-performing system (i.e.,GPT-4o) achieves only 60.9% accuracy using CoT prompting, leaving substantialroom for improvement. Moreover, while augmenting LLMs with external knowledgecan improve model performance (e.g., from 47.5% to 54.5% for Gemini-1.5-Pro),their accuracy remains significantly lower than the estimated human expertperformance of 92%. We believe that FinanceMath can advance future research inthe area of domain-specific knowledge retrieval and integration, particularlywithin the context of solving reasoning-intensive tasks.

FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains