当AI污染网络时检索崩溃
Retrieval Collapses When AI Pollutes the Web
AI总结：
研究发现AI生成内容污染导致检索系统崩溃，需引入检索意识策略以防止质量下降。
摘要：
随着人工智能生成内容在互联网上的快速普及，信息检索系统面临结构性风险，因为搜索引擎和检索增强生成（RAG）系统越来越多地消耗由大型语言模型（LLMs）生成的证据。我们将这种生态系统层面的失败模式称为检索崩溃，这是一个两阶段过程：（1）AI生成的内容主导搜索结果，削弱了来源多样性；（2）低质量或对抗性内容渗透到检索管道中。我们通过受控实验分析了这种动态，涉及高质量的SEO风格内容和对抗性编写的內容。在SEO场景中，67%的池污染导致超过80%的暴露污染，形成一个同质化但看似健康的狀態，尽管依赖合成来源，回答准确性仍保持稳定。相反，在对抗性污染下，基准如BM25暴露了约19%的有害内容，而基于LLM的排序器显示出更强的抑制能力。这些发现突显了检索管道悄然转向合成证据的风险，以及需要检索意识策略来防止Web基础系统中质量下降的自我强化循环。
英文摘要：
The rapid proliferation of AI-generated content on the Web presents a structural risk to information retrieval, as search engines and Retrieval-Augmented Generation (RAG) systems increasingly consume evidence produced by the Large Language Models (LLMs). We characterize this ecosystem-level failure mode as Retrieval Collapse, a two-stage process where (1) AI-generated content dominates search results, eroding source diversity, and (2) low-quality or adversarial content infiltrates the retrieval pipeline. We analyzed this dynamic through controlled experiments involving both high-quality SEO-style content and adversarially crafted content. In the SEO scenario, a 67\% pool contamination led to over 80\% exposure contamination, creating a homogenized yet deceptively healthy state where answer accuracy remains stable despite the reliance on synthetic sources. Conversely, under adversarial contamination, baselines like BM25 exposed $\sim$19\% of harmful content, whereas LLM-based rankers demonstrated stronger suppression capabilities. These findings highlight the risk of retrieval pipelines quietly shifting toward synthetic evidence and the need for retrieval-aware strategies to prevent a self-reinforcing cycle of quality decline in Web-grounded systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
