AutoCrawler is a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding and aims to assist crawlers in handling diverse and changing web environments more efficiently. This work introduces a crawler generation task for vertical information web pages and proposes the paradigm of combining LLMs with crawlers, which supports the adaptability of traditional methods and enhances the performance of generative agents in open-world scenarios. Generative agents, empowered by large language models, suffer from poor performance and reusability in open-world scenarios.
train models for processing documents based on specific needs and requirements. It offers capabilities such as entity recognition, key information extraction, and data validation,
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