WebWalker: Benchmarking LLMs in Web Traversal

Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Xi Ze Kun, Runnan Fang, zhoudeyu, pengjun xie, Fei Huang

WebWalker: Benchmarking LLMs in Web Traversal: 23 upvotes on Hugging Face Daily Papers, #8 of 12 papers on 2025-01-14. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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