WebWalker:在网络遍历中对LLM进行基准测试
WebWalker: Benchmarking LLMs in Web Traversal
January 13, 2025
作者: Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Deyu Zhou, Pengjun Xie, Fei Huang
cs.AI
摘要
检索增强生成(RAG)在开放领域问答任务中展现出卓越性能。然而,传统搜索引擎可能检索到表面内容,限制了LLM处理复杂、多层信息的能力。为解决这一问题,我们引入了WebWalkerQA,一个旨在评估LLM执行网页遍历能力的基准。它评估LLM遍历网站子页面系统提取高质量数据的能力。我们提出了WebWalker,这是一个模拟人类网页导航的多智能体框架,通过探索-评论家范式实现。广泛的实验结果表明,WebWalkerQA具有挑战性,并展示了RAG与WebWalker结合在真实场景中的水平和垂直整合的有效性。
English
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.Summary
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