title: WebWalker: Benchmarking LLMs in Web Traversal
summary: Retrieval-augmented generation (RAG) demonstrates remarkable performance
  across tasks in open-domain question-answering. However, traditional search
  engines may retrieve shallow content, limiting the ability of LLMs to handle
  complex, multi-layered information. To address it, we introduce WebWalkerQA, a
  benchmark designed to assess the ability of LLMs to perform web traversal. It
  evaluates the capacity of LLMs to traverse a website's subpages to extract
  high-quality data systematically. We propose WebWalker, which is a multi-agent
  framework that mimics human-like web navigation through an explore-critic
  paradigm. Extensive experimental results show that WebWalkerQA is challenging
  and demonstrates the effectiveness of RAG combined with WebWalker, through the
  horizontal and vertical integration in real-world scenarios.
paper_authors._id: 6785df7a934f275b48366bdf
paper_authors.name: Jialong Wu
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paper_authors.name: Wenbiao Yin
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paper_authors.name: Yong Jiang
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paper_authors.fullname: Yong Jiang
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paper_authors.name: Zhenglin Wang
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paper_authors.fullname: Zhenglin Wang
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paper_authors.name: Zekun Xi
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paper_authors.fullname: Xi Ze Kun
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paper_authors.name: Runnan Fang
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paper_authors.fullname: Runnan Fang
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paper_authors.user: Runnaning
paper_authors._id: 6785df7a934f275b48366be5
paper_authors.name: Deyu Zhou
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paper_authors.fullname: zhoudeyu
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paper_authors.user: zhoudeyu
paper_authors._id: 6785df7a934f275b48366be6
paper_authors.name: Pengjun Xie
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paper_authors._id: 63a091e42fabbbb89991f5ce
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paper_authors.fullname: pengjun xie
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paper_authors.user: xpjandy
paper_authors._id: 6785df7a934f275b48366be7
paper_authors.name: Fei Huang
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paper_authors.fullname: Fei Huang
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paper_title: WebWalker: Benchmarking LLMs in Web Traversal
paper_summary: Retrieval-augmented generation (RAG) demonstrates remarkable performance
  across tasks in open-domain question-answering. However, traditional search
  engines may retrieve shallow content, limiting the ability of LLMs to handle
  complex, multi-layered information. To address it, we introduce WebWalkerQA, a
  benchmark designed to assess the ability of LLMs to perform web traversal. It
  evaluates the capacity of LLMs to traverse a website's subpages to extract
  high-quality data systematically. We propose WebWalker, which is a multi-agent
  framework that mimics human-like web navigation through an explore-critic
  paradigm. Extensive experimental results show that WebWalkerQA is challenging
  and demonstrates the effectiveness of RAG combined with WebWalker, through the
  horizontal and vertical integration in real-world scenarios.
paper_ai_keywords: Retrieval-augmented generation (RAG) open-domain question-answering LLMs web traversal WebWalkerQA multi-agent framework explore-critic paradigm WebWalker