Papers by Yuanhan Zhang
Video-MMMU: Evaluating Knowledge Acquisition from Multidisciplinary Professional Videos (2026.acl-long)
Copied to clipboard
| Challenge: | Existing video benchmarks do not evaluate the knowledge acquisition capabilities of Large Multimodal Models (LMMs) existing video benchmark focuses on static, general visual understanding tasks, without evaluating whether models can acquire knowledge dynamically. |
| Approach: | They propose a multi-modal, multi-discipline, multitrack benchmark that evaluates Large Multimodal Models’ ability to acquire knowledge from college-level, educational videos. |
| Outcome: | The proposed benchmark reveals a substantial gap between human learners and current Large Multimodal Models (LMMs) and focuses on improving their learning efficiency. |
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2025.findings-naacl)
Copied to clipboard
Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, Ziwei Liu
| Challenge: | Current large foundational models have demonstrated transformative capabilities, approaching or surpassing human-level performances in many tasks. |
| Approach: | They propose a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models to promote transparent and reproducible evaluations. |
| Outcome: | The proposed framework has 50 tasks and more than 10 models to promote transparent and reproducible evaluations. |
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward (2025.naacl-long)
Copied to clipboard
Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander G Hauptmann, Yonatan Bisk, Yiming Yang
| Challenge: | Existing studies have demonstrated that direct preference optimization (DPO) can be effective in generalizing large language models, but its effectiveness in video domain remains limited. |
| Approach: | They propose a framework that utilizes detailed video captions as a proxy of video content to enable language models to incorporate this information as supporting evidence for scoring video Question Answering (QA) predictions. |
| Outcome: | The proposed framework shows that it can be used to align language models with video content and improves performance on open-ended video QA tasks. |