Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912774888620032 |
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| author | Lee, Dongjun Lee, Juyong Kim, Kyuyoung Tack, Jihoon Shin, Jinwoo Teh, Yee Whye Lee, Kimin |
| author_facet | Lee, Dongjun Lee, Juyong Kim, Kyuyoung Tack, Jihoon Shin, Jinwoo Teh, Yee Whye Lee, Kimin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents Lee, Dongjun Lee, Juyong Kim, Kyuyoung Tack, Jihoon Shin, Jinwoo Teh, Yee Whye Lee, Kimin Computation and Language 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. |
| title | Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.10689 |