Summary of Agentoccam: a Simple Yet Strong Baseline For Llm-based Web Agents, by Ke Yang et al.
AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents
by Ke Yang, Yao Liu, Sapana Chaudhary, Rasool Fakoor, Pratik Chaudhari, George Karypis, Huzefa Rangwala
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 presents a novel approach to developing autonomous web agents that utilize large language models (LLMs) for personalized and standardized tasks. By refining the observation and action space of an LLM-based web agent, the authors demonstrate significant improvements in task performance on WebArena, a benchmark featuring general-purpose web interaction tasks. The proposed method, AgentOccam, outperforms previous state-of-the-art methods by 9.8% (+29.4%) and concurrent work by 5.9% (+15.8%), with a notable boost in success rate of 26.6 points (+161%). This achievement is remarkable, as it is achieved without using in-context examples, new agent roles, online feedback or search strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study highlights the potential of LLMs for web tasks and emphasizes the importance of carefully tuning observation and action spaces for LLM-based agents to achieve better performance. The proposed method has significant implications for various applications, including web automation and agent grounding scenarios. |
Keywords
» Artificial intelligence » Grounding
