检测生成式搜索引擎是否引用了人工智能生成的虚假来源。
Synthetic Sources?: Auditing Generative Search Engine Citations for Evidence of AI-Generated Sources
- 用712个真实问题测试四大引擎，审计其引用来源的真实性。
- 约16%的引用来源为AI生成，且集中在少数特定网站域名。
- 引擎过度依赖少量高频引用域，多数来源几乎无人引用。
随着对话式大语言模型通过生成式搜索引擎从网络中检索、整合并引用信息来回答用户问题，信息获取变得更为便捷。然而，随着网络上AI生成内容激增，这些引擎能否可靠地避免引用合成来源（即AI生成的内容）尚不明确。若无法做到，则可能误导用户将生成式搜索引擎中的信息视为与权威或官方来源等同，带来潜在风险。本文针对四种生成式搜索引擎（ChatGPT、Copilot、Gemini、Perplexity），使用总计712个涵盖政治、健康和环境等公共重要领域的实际人类查询进行审计。结果显示，所有引擎均存在引用AI生成来源的证据，占比约16%，并识别出被频繁引用的若干关键网站域名。此外，观察发现这些引擎倾向于重复引用少数几个领域，而绝大多数来源被极少引用。本研究有助于揭示生成式搜索引擎的风险，提升公众对其局限性的认知，并推动改进信息质量和系统治理措施。
原文摘要 · Abstract (English)
The growing accessibility of Large Language Models via conversational interfaces capable of responding to users' questions by drawing on, synthesizing, and citing information from the web (i.e., Generative Search Engines) has simplified the information-seeking process for users. However, with the proliferation of AI-generated content on the web, it is unclear whether these engines can reliably omit citing synthetic sources (i.e., AI-generated sources). Should these engines be unable to do so, this puts users at risk of harm by treating information from AI-generated sources synthesized in responses of generative search engines as equivalent to information from authoritative or official sources. In a step towards identifying whether AI-generated sources are being cited by these engines, this work presents an audit of four generative search engines (ChatGPT, Copilot, Gemini, Perplexity) using a total of 712 real-world human-generated queries spanning domains of public importance: politics, health, and the environment. Our findings show evidence of AI-generated sources being cited across all four generative search engines (~16% of cited sources) and identifies key source web domains these sources belong to that are frequently cited across these engines and topics. In addition, we observed that generative search engines include a somewhat narrow set of repeatedly cited domains while predominantly surfacing a large number of minimally cited domains in responses to users' queries. These findings contribute to the growing body of work on assessing the risks of generative search engines with the objective of increasing public awareness of their limitations and encouraging appropriate measures to improve information quality and governance of these systems.
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