Generative Information Retrieval
Open accessAbstract
Historically, information retrieval systems have all followed the same paradigm: information seekers frame their needs in the form of a short query, the system selects a small set of relevant results from a corpus of available documents, rank-orders the results by decreasing relevance, possibly excerpts a responsive passage for each result, and returns a list of references and excerpts to the user. Retrieval systems typically did not attempt fusing information from multiple documents into an answer and displaying that answer directly. This was largely due to available technology: at the core of each retrieval system is an index that maps lexical tokens or semantic embeddings to document identifiers. Indices are designed for retrieving responsive documents; they do not support integrating these documents into a holistic answer.
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With too much context supplied at once, the LLM is at greater risk of generating 'hallucinations'.
“With too much context supplied at once, the LLM is at greater risk of generating 'hallucinations' (or, semantically plausible but factually incorrect text) (Najork 2023; Liu et al. 2023).”
From PANGeA: Procedural Artificial Narrative Using Generative AI for Turn-Based, Role-Playing Video Games (2024) · cites this paper as Najork 2023
The Abstract identifies grounding generated answers as an open challenge, but does not discuss whether supplying too much context increases hallucination risk. Searches for excessive context and factually incorrect answers found no passage addressing that relationship. (LLM, conf=0.76)
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Rethinking search: making domain experts out of dilettantes
Metzler, D., Tay, Y., Bahri, D., & Najork, M. (2021) · ACM SIGIR Forum · doi:10.1145/3476415.3476428 · Cited by 112 · see citing works
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Research works printed but not linked: 2
- Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How Much Knowledge Can You Pack Into the Parameters of a Language Model?. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 5418-5426.
- Chirag Shah and Emily M. Bender. 2022. Situating Search. In Proceedings of the 2022 Conference on Human Information Interaction and Retrieval. 221-232.
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Advancing the search frontier with AI agents
White, R. W. (2024) · Communications of the ACM · doi:10.1145/3655615 · Cited by 55 · see citing works
PANGeA: Procedural Artificial Narrative Using Generative AI for Turn-Based, Role-Playing Video Games
Buongiorno, S., Klinkert, L., Zhuang, Z., Chawla, T., & Clark, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v20i1.31876 · Cited by 37 · see citing works
Recent Advances in Generative Information Retrieval
Tang, Y., Zhang, R., Ren, Z., Guo, J., & de Rijke, M. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3661379 · Cited by 4 · see citing works
- Authors
- Najork, M.
- Affiliations
- Google (United States) (US)
- Corresponding author
- Najork, Marc
- ORCID
- Najork, Marc
- Venue
- Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2023-07-18
- Pages
- 1-1
- OpenAlex
- W4384641525
- Cited by
- 22
- References
- 1
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 10 · 2023: 2
- FWCI
- 5.09
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- 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
- answer generation, relevance ranking, document indexing, semantic embeddings
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