Rethinking search: making domain experts out of dilettantes
Open accessAbstract
When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts - they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice.
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Metzler et al. discussed several paradigms in which pre-trained language models can help IR systems answer user's information needs directly without listing references.
“Metzler et al. [133] has discussed several paradigms in which pre-trained language models can help IR systems answer user's information needs directly without listing references.”
From Foundations of GenIR (2025) · cites this paper as [133]
The Introduction (p. 2) says pre-trained language models can generate prose responsive to an information need, and Section 3 (pp. 6–7) proposes model-based IR in place of traditional retrieval and ranking. This supports direct answers rather than merely listing references, but the paper presents one proposed model-based paradigm, not several such paradigms; it also stresses the need to justify answers with supporting documents. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe shift toward generative information access is instructive: a vision articulated in IR was realized mainly through LLMs developed outside the IR community.
“The shift toward generative information access is instructive: a vision articulated in IR [39] was realized mainly through LLMs developed outside the IR community (Section 1).”
From Agentic Memory Management Is the Next Frontier of Information Retrieval (2026) · cites this paper as [39]
Large language models (LLMs) are increasingly capable of providing users with advice in a wide range of professional domains, including legal advice.
“Large language models (LLMs) are increasingly capable of providing users with advice in a wide range of professional domains, including legal advice.”
From (A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice (2024) · cites this paper as [50]
LLMs can 'hallucinate' answers not grounded in their training data, severely compromising reliability.
“Most critically, as statistical models, LLMs can 'hallucinate' answers not grounded in their training data, severely compromising reliability [45, 50, 70].”
From (A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice (2024) · cites this paper as [50]
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Mapping Knowledge Flows in ExploratoryWeb Searches
Tibau, M., Siqueira, S. W. M., & Nunes, B. P. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.669 · Cited by 1 · see citing works
Design Challenges for a Multi-Perspective Search Engine
Chen, S., Liu, S., Uyttendaele, X., Zhang, Y., Bruno, W., & Roth, D. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.22 · Cited by 3 · see citing works
Foundation Models for Text Generation
Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_6 · Cited by 2 · see citing works
Query reformulation system based on WordNet and word vectors clusters
Jumde, A. S., & Keskar, R. B. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-236296 · Cited by 1 · see citing works
RetractedGen-IR @ SIGIR 2024: The Second Workshop on Generative Information Retrieval
Bénédict, G., Zhang, R., Metzler, D., Yates, A., & Jiang, Z. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657982 · Cited by 1 · see citing works
Self-citationIntellectNavigator: Enhancing Search Tools with LLMs-Powered Query Instruction
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An Open Source Direct Messaging and Enhanced Recommendation System for Yioop
Mallya, A. D. (2021) · doi:10.31979/etd.474u-r5rn · Cited by 1 · see citing works
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Doc-to-Doc Recommender for Medical Literature with Similarity of Molecule Graphs
Qu, J., Jing, Z., Li, X., Yu, Y., Lin, H., Lyu, X., & Tang, Z. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9735184
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Negotiating becoming: a Nietzschean critique of large language models
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The Evolution of Information Seeking Practices and Technologies and the Future of the Library
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Generative Insights Unveiling AI's Evolution and Algorithms
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Foundations of Generative Information Retrieval
Ai, Q., Zhan, J., & Liu, Y. (2024) · The information retrieval series · doi:10.1007/978-3-031-73147-1_2
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Translational Generative Retrieval via Potential Query Generation
Guo, Y., Liu, T., Sheng, J., Ma, D., Sun, M., & Tian, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888918
User control of search algorithms would improve science
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Generative Information Retrieval
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Self-citation
- Authors
- Metzler, D., Tay, Y., Bahri, D., & Najork, M.
- Affiliations
- Google (United States) (US)
- Venue
- ACM SIGIR Forum
- Published
- 2021-06
- Volume
- 55
- Issue
- 1
- Pages
- 1-27
- ISSN
- 0163-5840 (ISSN-L)
- OpenAlex
- W3185146124
- Cited by
- 112
- References
- 103
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 32 · 2023: 31 · 2022: 12 · 2021: 3
- FWCI
- 17.63
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGIR Forum (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- information retrieval, question answering, pre-trained language models, hallucinations, information needs, retrieval-augmented generation, supporting documents, retrieval-augmented question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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