We introduce $\color{Blue}{\text{MMVU}}$, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. $\color{Blue}{\text{MMVU}}$ includes 3,000 expert-annotated questions spanning 27 subjects across four core disciplines: Science, Healthcare, Humanities & Social Sciences, and Engineering. Compared to prior benchmarks, $\color{Blue}{\text{MMVU}}$ features three key advancements. First, it challenges models to apply domain-specific knowledge and perform expert-level reasoning to analyze specialized-domain videos, moving beyond the basic visual perception typically assessed in current video benchmarks. Second, each example is annotated by human experts from scratch. We implement strict data quality controls to ensure the high quality of the dataset. Finally, each example is enriched with expert-annotated reasoning rationals and relevant domain knowledge, facilitating in-depth analysis. We conduct an extensive evaluation of 36 frontier multimodal foundation models on $\color{Blue}{\text{MMVU}}$. The latest System-2-capable models, o1 and Gemini 2.0 Flash Thinking, achieve the highest performance among the tested models. However, they still fall short of matching human expertise. Through in-depth error analyses and case studies, we offer actionable insights for future advancements in expert-level, knowledge-intensive video understanding for specialized domains.

Yilun Zhao, Yitao Long, Hongjun Liu, Ryo Kamoi, Linyong Nan, Lyuhao Chen, Yixin Liu, Xiangru Tang, Rui Zhang, Arman Cohan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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 ContributionQuestion: 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

Yilun Zhao, Yitao Long, Tintin Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Xiangru Tang, Yiming Zhang, Chen Zhao, Arman Cohan. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.