Summary of From Grounding to Planning: Benchmarking Bottlenecks in Web Agents, by Segev Shlomov et al.
From Grounding to Planning: Benchmarking Bottlenecks in Web Agents
by Segev Shlomov, Ben wiesel, Aviad Sela, Ido Levy, Liane Galanti, Roy Abitbol
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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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 A novel analysis is presented to improve the performance of general web-based agents in real-world applications. Current state-of-the-art models exhibit extremely low accuracy, emphasizing the need for a deeper understanding of these agents’ components. The study decomposes agents into planning and grounding components, refining experiments on the Mind2Web dataset to propose separate benchmarks for each. Surprisingly, the findings suggest that grounding is not the primary bottleneck, but rather the planning component, which degrades performance. The work provides new insights and practical suggestions for enhancing agent capabilities, ultimately paving the way for more reliable agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Websites are getting smarter, but talking to them can be tricky! Researchers have been trying to make computers better at understanding what we want when interacting with websites, but so far, they haven’t been very good. This paper takes a closer look at how these “web agents” work and finds that most of the problems come from making plans about what actions to take on the website, rather than figuring out what words mean. By separating these two parts and testing them separately, scientists hope to make better computers that can help us more easily talk to websites in the future. |
Keywords
» Artificial intelligence » Grounding
