Synthetic Sources?: Auditing Generative Search Engine Citations for Evidence of AI-Generated Sources

• URL: http://arxiv.org/abs/2605.23684v1
• Date: Fri, 22 May 2026 14:33:52 GMT
• Title: Synthetic Sources?: Auditing Generative Search Engine Citations for Evidence of AI-Generated Sources
• Abstract summary: It is unclear whether generative search engines can reliably omit citing synthetic sources.<n>This work presents an audit of four generative search engines using a total of 712 real-world human-generated queries.<n>Our findings show evidence of AI-generated sources being cited across all four generative search engines.
• Score: 3.2065257821139195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Answer Bubbles: Information Exposure in AI-Mediated Search [14.377773240202849]
  We examine responses to 11,000 real search queries across four systems -- vanilla GPT, Search GPT, Google AI Overviews, and traditional Google Search.<n>We find that generative search systems exhibit significant textitsource-selection biases in their citations.
  arXiv  Detail & Related papers  (2026-03-17T05:41:39Z)
• DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence [50.97612134791782]
  Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing citation practices.<n>We introduce DeepTRACE, a novel sociotechnically grounded audit framework that turns prior community-identified failure cases into eight measurable dimensions spanning answer text, sources, and citations.
  arXiv  Detail & Related papers  (2025-09-02T00:32:38Z)
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents [96.65646344634524]
  Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
  arXiv  Detail & Related papers  (2025-06-23T17:27:19Z)
• Information Retrieval in the Age of Generative AI: The RGB Model [77.96475639967431]
  This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.<n>We propose a model to characterize the generation, indexing, and dissemination of information in response to new topics.<n>Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation.
  arXiv  Detail & Related papers  (2025-04-29T10:21:40Z)
• Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses [32.49468716515915]
  Large Language Model (LLM)-based applications are graduating from research prototypes to products serving millions of users. A prominent example is the appearance of Answer Engines: LLM-based generative search engines supplanting traditional search engines.
  arXiv  Detail & Related papers  (2024-10-15T00:50:31Z)
• Generative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authority [2.860575804107195]
  This paper reports on an audit study of generative AI systems (ChatGPT, Bing Chat, and Perplexity) which investigates how these new search engines construct responses. We collected system responses using a set of 48 authentic queries for 4 topics over a 7-day period and analyzed the data using sentiment analysis, inductive coding and source classification. Results provide an overview of the nature of system responses across these systems and provide evidence of sentiment bias based on the queries and topics, and commercial and geographic bias in sources.
  arXiv  Detail & Related papers  (2024-05-22T22:09:32Z)
• Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions [89.35345649303451]
  Generative search engines have the potential to transform how people seek information online. But generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system.
  arXiv  Detail & Related papers  (2024-02-25T11:22:19Z)
• Search Engines Post-ChatGPT: How Generative Artificial Intelligence Could Make Search Less Reliable [2.7557703490816268]
  We discuss the evolving nature of search engines, as they begin to generate, index, and distribute content created by generative artificial intelligence (GenAI) Our discussion highlights challenges in the early stages of GenAI integration, particularly around factual inconsistencies and biases.
  arXiv  Detail & Related papers  (2024-02-18T21:10:18Z)
• GEO: Generative Engine Optimization [50.45232692363787]
  We formalize the unified framework of generative engines (GEs) GEs use large language models (LLMs) to gather and summarize information to answer user queries. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them. We introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses.
  arXiv  Detail & Related papers  (2023-11-16T10:06:09Z)
• Evaluating Verifiability in Generative Search Engines [70.59477647085387]
  Generative search engines directly generate responses to user queries, along with in-line citations. We conduct human evaluation to audit four popular generative search engines. We find that responses from existing generative search engines are fluent and appear informative, but frequently contain unsupported statements and inaccurate citations.
  arXiv  Detail & Related papers  (2023-04-19T17:56:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.