AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation

Anonymous

Abstract: Web automation is a significant technique that accomplishes complicated web tasks through automating common web actions, which enhance operational efficiency and reduce the need for manual intervention. Traditional methods, such as wrappers, suffer from limited adaptability and scalability when facing new website. On the other hand, generative agents empowered by Large Language Models (LLMs) exhibit poor performance and reusability in open-world scenarios. In this work, we introduce a crawler generation task for vertical information web pages and the paradigm of combining LLMs with crawlers, which helps crawlers handle diverse and changing web environments more efficiently. We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding. Through top-down and step-back operations, AutoCrawler can learn from erroneous actions and continuously prune HTML for better action generation. We conduct comprehensive experiments with multiple LLMs and demonstrate the effectiveness of our framework.

Paper Type: long

Research Area: NLP Applications

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English