Summary of Webvoyager: Building An End-to-end Web Agent with Large Multimodal Models, by Hongliang He et al.
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models
by Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, Dong Yu
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 paper introduces WebVoyager, a Large Multimodal Model (LMM) powered web agent that can complete user instructions end-to-end by interacting with real-world websites. The authors establish a new benchmark using real-world tasks from 15 popular websites and develop an automatic evaluation protocol leveraging multimodal understanding abilities of GPT-4V to evaluate open-ended web agents. WebVoyager achieves a 59.1% task success rate on the benchmark, surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-only) setups. The proposed automatic evaluation metric achieves 85.3% agreement with human judgment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new kind of web agent that can understand and complete tasks on real websites. Right now, most web agents are only good at doing one thing, like searching or chatting. But this new agent, called WebVoyager, is special because it can do lots of things and work with different types of information, like text, images, and more. The people who wrote the paper also came up with a way to test how well these agents do their jobs. |
Keywords
» Artificial intelligence » Gpt
