Recent advances in large language models (LLMs) have led to a growing interest in developing LLM-based agents for automating web tasks. However, these agents often struggle with even simple tasks on real-world websites due to their limited capability to understand and process complex web page structures. In this work, we introduce LCoW, a framework for Learning language models to Contextualize complex Web pages into a more comprehensible form, thereby enhancing decision making by LLM agents. LCoW decouples web page understanding from decision making by training a separate contextualization module to transform complex web pages into comprehensible format, which are then utilized by the decision-making agent. We demonstrate that our contextualization module effectively integrates with LLM agents of various scales to significantly enhance their decision-making capabilities in web automation tasks. Notably, LCoW improves the success rates of closed-source LLMs (e.g., Gemini-1.5-flash, GPT-4o, Claude-3.5-Sonnet) by an average of 15.6%, and demonstrates a 23.7% average improvement in success rates for open-source LMs (e.g., Llama-3.1-8B, Llama-3.1-70B) on the WorkArena benchmark. Moreover, the Gemini-1.5-flash agent with LCoW achieves state-of-the-art results on the WebShop benchmark, outperforming human experts. The relevant code materials are available at our project page: https://lcowiclr2025.github.io.

翻译：近年来，大型语言模型（LLM）的进展引发了人们对开发基于LLM的智能体以自动化网络任务的日益增长的兴趣。然而，由于理解和处理复杂网页结构的能力有限，这些智能体即使在现实世界网站上的简单任务中也常常遇到困难。在本工作中，我们提出了LCoW框架，该框架通过学习语言模型将复杂网页上下文化为更易理解的形式，从而增强LLM智能体的决策能力。LCoW通过训练一个独立的上下文化模块，将复杂网页转换为可理解的格式，从而将网页理解与决策制定解耦，转换后的内容随后由决策制定智能体利用。我们证明，我们的上下文化模块能够与不同规模的LLM智能体有效集成，显著提升其在网络自动化任务中的决策能力。值得注意的是，在WorkArena基准测试中，LCoW将闭源LLM（例如Gemini-1.5-flash、GPT-4o、Claude-3.5-Sonnet）的成功率平均提高了15.6%，并将开源LM（例如Llama-3.1-8B、Llama-3.1-70B）的成功率平均提升了23.7%。此外，配备LCoW的Gemini-1.5-flash智能体在WebShop基准测试中取得了最先进的结果，超越了人类专家。相关代码材料可在我们的项目页面获取：https://lcowiclr2025.github.io。