Summary of Wilbur: Adaptive In-context Learning For Robust and Accurate Web Agents, by Michael Lutz et al.
WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents
by Michael Lutz, Arth Bohra, Manvel Saroyan, Artem Harutyunyan, Giovanni Campagna
First submitted to arxiv on: 8 Apr 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 Wilbur, an approach that tackles the challenging problem of achieving both generalization and accuracy in web agent research. To overcome high variance in website structure, Wilbur employs a differentiable ranking model and novel instruction synthesis technique to optimally populate a large language model’s prompt with task demonstrations from previous runs. Additionally, the paper proposes an intelligent backtracking mechanism that learns and recovers from mistakes. The approach is trained on data from a generative auto-curriculum, which samples representative goals from a large language model, runs the agent, and automatically evaluates it without manual annotation. Wilbur achieves state-of-the-art results on the WebVoyager benchmark, beating text-only models by 8% overall, and up to 36% on certain websites. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wilbur is a new approach for web agents that helps them understand how to do tasks better. Right now, most approaches don’t work well because websites are very different from each other. Wilbur uses a special way of teaching a large language model what to do by giving it examples of how to do things on the internet. It also has a clever way of learning from its mistakes and trying again. This approach is really good at figuring out how to do tasks on the web, and it even beats other approaches that use more information than just text! |
Keywords
» Artificial intelligence » Generalization » Large language model » Prompt
