KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains

We introduce FinanceMATH, 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, FinanceMATH includes 1,200 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 51 LLMs with both Chainof-Thought and Program-of-Thought prompting methods. Our experimental results reveal that the current best-performing system (i.e., GPT-4o) achieves only 60.9% accuracy using CoT prompting, leaving substantial room for improvement. Moreover, while augmenting LLMs with external knowledge can improve model performance (e.g., 47.5% → 54.5% for Gemini-1.5-Pro), their accuracy remains significantly lower than the estimated human expert performance of 92%. We believe that Fi-nanceMATH can advance future research in the area of domain-specific knowledge retrieval and integration, particularly within the context of solving reasoning-intensive tasks. * Equal Contribution Question: In 2018, Company A had a passive equity ownership interest of 15% in Company B. By the close of 2018, Company A decided to increase its ownership in Company B to 50%, effective as of 1st January 2019, through a cash purchase. There have been no financial transactions between Company A and Company B. Based on the data in the following table with the financial statements for both companies, what would be the changes in the total liabilities for Company A under the proportionate consolidation method from 2018 to 2019? Company A