WebWalker: Benchmarking LLMs in Web Traversal
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.
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Jialong Wu
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Jialong Wu
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Wenbiao Yin
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Yong Jiang
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Yong Jiang
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Zhenglin Wang
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Zhenglin Wang
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Zekun Xi
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Xi Ze Kun
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Runnan Fang
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Runnan Fang
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Deyu Zhou
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zhoudeyu
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Pengjun Xie
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pengjun xie
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Fei Huang
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Fei Huang
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WebWalker: Benchmarking LLMs in Web Traversal
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.
WebWalkerQA assesses LLMs' ability to traverse websites for high-quality data, showing enhancements when combined with RAG using the WebWalker multi-agent framework.
Retrieval-augmented generation RAG benchmark LLMs web traversal explore-critic paradigm WebWalker