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Search Engines in the AI Era: A Qualitative Understanding to the False Promise of Factual and Verifiable Source-Cited Responses in LLM-based Search
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“A qualitative study with 21 expert users observed substantially fewer source interactions with an LLM-based answer engine compared to a traditional search engine.”
From AI Overviews in Academic Search: Evaluating AI-generated Summaries of Search Results in a Domain-specific Search Engine (2026) · cites this paper as Narayanan Venkit et al., 2025
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“This gap [5] is evident in the conceptualization of terms like harm [4, 67], sentiment [61], and hallucination [62].”
Taxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
Evaluating Verifiability in Generative Search Engines
Liu, N., Zhang, T., & Liang, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.467 · Cited by 143
Evaluating Verifiability in Generative Search Engines
Liu, N. F., Zhang, T., & Liang, P. (2023) · arXiv · Cited by 143 · cited 1×
“Second, prior work [33, 38] has also shown limitations of answer engines’ ability to assess the accuracy of citations within an answer.”
Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking
Sharma, N., Liao, Q. V., & Xiao, Z. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642459 · Cited by 132 · cited 2×
“[26, 58].”
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Cited by
Sorted by citations
Schott, K., Silva, K., Frommholz, I., Mayr, P., Kern, D., & Hienert, D. (2026) · arXiv · doi:10.48550/arxiv.2607.03421
The impact of generative AI search result design on user perceptions and information adoption
Li, A. (2026) · Proceedings of the 2026 Conference on Human Information Interaction and Retrieval · doi:10.1145/3786304.3787937
Metadata
- Authors
- Narayanan Venkit, P., Laban, P., Zhou, Y., Mao, Y., & Wu, C.-S.
- Affiliations
- Pennsylvania State University, University Park, USA
- Salesforce AI Research, Palo Alto, USA
- Venue
- Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2025-06-23
- Pages
- 1325-1340
- Cited by
- 23
- References
- 8
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