Summary of Mmworld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos, by Xuehai He et al.
MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos
by Xuehai He, Weixi Feng, Kaizhi Zheng, Yujie Lu, Wanrong Zhu, Jiachen Li, Yue Fan, Jianfeng Wang, Linjie Li, Zhengyuan Yang, Kevin Lin, William Yang Wang, Lijuan Wang, Xin Eric Wang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new benchmark, MMWorld, to assess the abilities of Multimodal Language Language Models (MLLMs) in interpreting and reasoning about complex real-world dynamics through multimodal video understanding. The benchmark distinguishes itself with two unique advantages: multi-discipline coverage across various disciplines and multi-faceted reasoning, including explanation, counterfactual thinking, future prediction, etc. MMWorld consists of a human-annotated dataset to evaluate MLLMs and a synthetic dataset to analyze them within a single modality of perception. The evaluation includes 2 proprietary and 10 open-source MLLMs, which struggle on MMWorld, showing large room for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MMWorld is a new way to test how well language models can understand complex videos from different fields like science, art, and history. It’s like a big quiz to see if the models can answer questions about what they’ve seen in the video. The quiz has many different types of questions, like explaining what happened or predicting what might happen next. There are thousands of videos with questions and answers, and it even compares how well humans do versus the language models. |
