mlsanityData to 7 Oct 2026

Large Language Models as Search Engines: Societal Challenges

arXiv 2512.08946 ↗First posted 24 Nov 2025cs.CY cs.CLcited by 3

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Abstract (arXiv)

Large Language Models (LLMs) may one day replace search engines as the primary portal to information on the Web. In this article, we investigate the societal challenges that such a change could bring. We focus on the roles of LLM Providers, Content Creators, and End Users, and identify 15 types of challenges. With each, we show current mitigation strategies -- both from the technical perspective and the legal perspective. We also discuss the impact of each challenge and point out future research opportunities.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 38 of its 112 references that are in the map. First posted on arXiv 24 Nov 2025.

Builds on

38 of 112 references.

References · 38

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  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
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  9. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 276
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  12. 2023Does Writing with Language Models Reduce Content Diversity?cited by 190
26 more
  1. 2022Will we run out of data? Limits of LLM scaling based on human-generated datacited by 187
  2. 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 186
  3. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 144
  4. 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
  5. 2023The Science of Detecting LLM-Generated Textscited by 126
  6. 2023Copyright Violations and Large Language Modelscited by 108
  7. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 101
  8. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 97
  9. 2023DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Modelscited by 75
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  11. 2024DE-COP: Detecting Copyrighted Content in Language Models Training Datacited by 72
  12. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 71
  13. 2024Lynx: An Open Source Hallucination Evaluation Modelcited by 55
  14. 2023Fairness-guided Few-shot Prompting for Large Language Modelscited by 53
  15. 2023Self-Guard: Empower the LLM to Safeguard Itselfcited by 52
  16. 2024Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinkingcited by 43
  17. 2024Proving membership in LLM pretraining data via data watermarkscited by 38
  18. 2024Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AIcited by 35
  19. 2024A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelismcited by 33
  20. 2023How to Protect Copyright Data in Optimization of Large Language Models?cited by 26
  21. 2024Double-I Watermark: Protecting Model Copyright for LLM Fine-tuningcited by 18
  22. 2024In-context learning agents are asymmetric belief updaterscited by 13
  23. 2024Reconfidencing LLMs from the Grouping Loss Perspectivecited by 11
  24. 2024Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answeringcited by 11
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Overreliance on AI in Information-seeking from Video Contentcited by 1
  2. 2026KARLA: Knowledge-base Augmented Retrieval for Language Modelscited by 0
  3. 2026WildSEEK: Evaluating Language Models for Information-Seekingcited by 0