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arXiv · 2501.07572

WebWalker: Benchmarking LLMs in Web Traversal

Jialong Wu·Wenbiao Yin·Yong Jiang·Zhenglin Wang·Zekun Xi·Runnan Fang·Linhai Zhang·Yulan He·Deyu Zhou·Pengjun Xie·Fei Huang
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Abstract

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, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang. 2025-01-13. WebWalker: Benchmarking LLMs in Web Traversal. https://arxiv.org/abs/2501.07572

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