Recent Advances in Generative Information Retrieval
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
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
Continual Learning for Generative Retrieval over Dynamic Corpora
Chen, J., Zhang, R., Guo, J., de Rijke, M., Chen, W., Fan, Y., & Cheng, X. (2023) · Proceedings of the 32nd ACM International Conference on Information and Knowledge Management · doi:10.1145/3583780.3614821 · Cited by 40 · see citing works
Self-citationMultiview Identifiers Enhanced Generative Retrieval
Li, Y., Yang, N., Wang, L., Wei, F., & Li, W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.366 · Cited by 37 · see citing works
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Chen, J., Zhang, R., Guo, J., de Rijke, M., Liu, Y., Fan, Y., & Cheng, X. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591631 · Cited by 32 · see citing works
Self-citation
Flagged first, then by citations
- Authors
- Tang, Y., Zhang, R., Ren, Z., Guo, J., & de Rijke, M.
- Affiliations
- Chinese Academy of Sciences (CN)
- Leiden University (NL)
- Corresponding author
- Tang, Yubao
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 363-368
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4393074659
- Cited by
- 2
- References
- 4
- Citations by year
- 2026: 1 · 2024: 1
- FWCI
- 0.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (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
- generative retrieval, sequence-to-sequence models, document identification, information retrieval
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.
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