mlsanityData to 7 Oct 2026

Search Engines Post-ChatGPT: How Generative Artificial Intelligence Could Make Search Less Reliable

arXiv 2402.11707 ↗First posted 18 Feb 2024cs.IR cs.AI cs.CYcited by 6

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Abstract (arXiv)

In this commentary, 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. We discuss how output from GenAI carries an unwarranted sense of credibility, while decreasing transparency and sourcing ability. Furthermore, search engines are already answering queries with error-laden, generated content, further blurring the provenance of information and impacting the integrity of the information ecosystem. We argue how all these factors could reduce the reliability of search engines. Finally, we summarize some of the active research directions and open questions.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

16 of 23 references.

References · 16

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  5. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
  6. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  7. 2023Evaluating Verifiability in Generative Search Enginescited by 297
  8. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  9. 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
  10. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 196
  11. 2023R-Tuning: Instructing Large Language Models to Say `I Don't Know'cited by 106
  12. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
4 more
  1. 2024Measuring Implicit Bias in Explicitly Unbiased Large Language Modelscited by 49
  2. 2024Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seekingcited by 13
  3. 2023"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Modelscited by 5
  4. 2024Google Scholar is manipulatablecited by 3

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 4.

6 papers.

  1. 2024Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responsescited by 15
  2. 2025News Source Citing Patterns in AI Search Systemscited by 9
  3. 2024Representation Bias of Adolescents in AI: A Bilingual, Bicultural Studycited by 4
  4. 2025Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answerscited by 3
  5. 2025CSP: A Simulator For Multi-Agent Ranking Competitionscited by 1
  6. 2025AI summaries in online search influence users' attitudescited by 1