Improving LLM-based Autonomous Web Agents with Filtering
Autonomous web agents, powered by Large Language Models (LLMs), have garnered significant attention for automating various web-based tasks with multi-step reasoning and decision-making capabilities. An open research question in the development of these agents lies in the format of the webpage input. Raw HTML source code, with its extensive and often irrelevant details, poses difficulties for LLMs with limited context windows. To address this challenge, we first reproduce baseline models such as GPT-3.5 and LLaMA-2-70B on the WebArena (Zhou et al., 2023) benchmark, identifying common failure modes. We then propose two retrieval strategies to filter out irrelevant context for LLM agents. We...
| Authors | Zhitong Guo, Jing Yu Koh, Ruiyu Li |
|---|---|
| Primary category | cs.CL |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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