让AI自动从复杂网页提取结构化数据，准确率超现有方法一倍。
WebLists: Extracting Structured Information From Complex Interactive Websites Using Executable LLM Agents
- 用可执行的LLM代理将网页操作转为可复用程序
- 在200个任务上达到66%召回率，比顶尖模型高35个百分点
- 适合需要批量提取网页数据的企业和开发者
现有网页智能体研究多聚焦于导航与交易任务，对大规模结构化数据提取关注不足。我们提出WebLists基准，涵盖4类常见商业与企业场景的200个数据提取任务，每个任务需智能体导航至页面、正确配置并提取具有明确定义模式的完整数据集。结果显示，具备搜索能力的LLM与当前最优网页智能体在这些任务上表现不佳，召回率仅为3%和31%，尽管其在问答任务中表现优异。为此，我们提出BardeenAgent框架，使网页智能体能将执行过程转化为可重复的程序，并在结构相似的页面上规模化重播。BardeenAgent首次利用HTML的规律性，通过构建通用的CSS选择器捕获页面所有相关项，并拟合数据提取操作。在WebLists基准上，该方法整体召回率达66%，超过最优基线两倍以上，且每行输出成本降低3倍。
原文摘要 · Abstract (English)
Most recent web agent research has focused on navigation and transaction tasks, with little emphasis on extracting structured data at scale. We present WebLists, a benchmark of 200 data-extraction tasks across four common business and enterprise use-cases. Each task requires an agent to navigate to a webpage, configure it appropriately, and extract complete datasets with well-defined schemas. We show that both LLMs with search capabilities and SOTA web agents struggle with these tasks, with a recall of 3% and 31%, respectively, despite higher performance on question-answering tasks. To address this challenge, we propose BardeenAgent, a novel framework that enables web agents to convert their execution into repeatable programs, and replay them at scale across pages with similar structure. BardeenAgent is also the first LLM agent to take advantage of the regular structure of HTML. In particular BardeenAgent constructs a generalizable CSS selector to capture all relevant items on the page, then fits the operations to extract the data. On the WebLists benchmark, BardeenAgent achieves 66% recall overall, more than doubling the performance of SOTA web agents, and reducing cost per output row by 3x.
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