Summary of Videowebarena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks, by Lawrence Jang et al.
VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks
by Lawrence Jang, Yinheng Li, Dan Zhao, Charles Ding, Justin Lin, Paul Pu Liang, Rogerio Bonatti, Kazuhito Koishida
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed VideoWebArena (VideoWA) benchmark aims to evaluate the capabilities of long-context multimodal agents for video understanding, filling the gap in existing agent benchmarks that focus on text or static image inputs. The benchmark consists of 2,021 web agent tasks based on manually crafted video tutorials totaling almost four hours of content. It defines a taxonomy of long-context video-based agent tasks with two main areas of focus: skill retention and factual retention. While the best model achieves 13.3% success on factual retention tasks and 45.8% on factual retention QA pairs, it still lags behind human performance at 73.9% and 79.3%, respectively. Long-context models perform worse with tutorials than without, exhibiting a 5% performance decrease in WebArena tasks and a 10.3% decrease in VisualWebArena tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video agents are being trained to learn or extract information from videos, which is different from learning from text and static images. To evaluate these agents, we created VideoWebArena (VideoWA), a benchmark that tests their ability to understand long videos. The benchmark includes 2,021 tasks based on video tutorials that add up to almost four hours of content. We tested the agents’ ability to retain skills and retrieve facts from the videos. While some agents did well, they still didn’t match human performance. This shows that we need to improve these agents’ abilities. |
