The DOI matches its Crossref record; 98 of the 103 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
Research works printed but not linked: 19
- Daniel Adiwardana , Minh-Thang Luong , David R So , Jamie Hall , Noah Fiedel , Romal Thoppilan , Zi Yang , Apoorv Kulshreshtha , Gaurav Nemade , Yifeng Lu , and Quoc V Le . Towards a humanlike open-domain chatbot. arXiv preprint arXiv:2001.09977 , 2020 . Daniel Adiwardana, Minh-Thang Luong, David R So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and Quoc V Le. Towards a humanlike open-domain chatbot. arXiv preprint arXiv:2001.09977, 2020. · arXiv
- 10.5555/347319.346290 · DOI
- Tom B. Brown , Benjamin Mann , Nick Ryder , Melanie Subbiah , Jared Kaplan , Prafulla Dhariwal , Arvind Neelakantan , Pranav Shyam , Girish Sastry , Amanda Askell , Sandhini Agarwal , Ariel Herbert-Voss , Gretchen Krueger , Tom Henighan , Rewon Child , Aditya Ramesh , Daniel M. Ziegler , Jeffrey Wu , Clemens Winter , Christopher Hesse , Mark Chen , Eric Sigler , Mateusz Litwin , Scott Gray , Benjamin Chess , Jack Clark , Christopher Berner , Sam McCandlish , Alec Radford , Ilya Sutskever , and Dario Amodei . Language models are few-shot learners . In Proceedings of the 34th Conference on Neural Information Processing Systems, NeurIPS '20 , 2020 . Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In Proceedings of the 34th Conference on Neural Information Processing Systems, NeurIPS '20, 2020.
- Avi Caciularu , Arman Cohan , Iz Beltagy , Matthew E Peters , Arie Cattan , and Ido Dagan . Cross-document language modeling. arXiv preprint arXiv:2101.00406 , 2021 . Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E Peters, Arie Cattan, and Ido Dagan. Cross-document language modeling. arXiv preprint arXiv:2101.00406, 2021. · arXiv
- 10.5555/1516224 · DOI
- Rajarshi Das , Shehzaad Dhuliawala , Manzil Zaheer , and Andrew McCallum . Multi-step retriever-reader interaction for scalable open-domain question answering . In Proceedings of the 7th International Conference on Learning Representations, ICLR '19 , 2019 . Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. Multi-step retriever-reader interaction for scalable open-domain question answering. In Proceedings of the 7th International Conference on Learning Representations, ICLR '19, 2019.
- 10.5555/3295222.3295377 · DOI
- 10.5555/2999792.2999959 · DOI
- 10.5555/1859417 · DOI
- Alec Radford , Jeffrey Wu , Rewon Child , David Luan , Dario Amodei , and Ilya Sutskever . Language models are unsupervised multitask learners . 2019 . Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019.
- 10.5555/3305890.3306024 · DOI
- 10.5555/3327345.3327399 · DOI
- Yi Tay , Mostafa Dehghani , Samira Abnar , Yikang Shen , Dara Bahri , Philip Pham , Jinfeng Rao , Liu Yang , Sebastian Ruder , and Donald Metzler . Long range arena: A benchmark for efficient transformers. arXiv preprint arXiv:2011.04006 , 2020 a. Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler. Long range arena: A benchmark for efficient transformers. arXiv preprint arXiv:2011.04006, 2020a. · arXiv
- 10.5555/3295222.3295349 · DOI
- 10.5555/2969442.2969540 · DOI
- Shuohang Wang , Mo Yu , Xiaoxiao Guo , Zhiguo Wang , Tim Klinger , Wei Zhang , Shiyu Chang , Gerry Tesauro , Bowen Zhou , and Jing Jiang . R3 : Reinforced ranker-reader for open-domain question answering . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI '18 , pages 5981 -- 5988 , 2018 a. Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerry Tesauro, Bowen Zhou, and Jing Jiang. R3: Reinforced ranker-reader for open-domain question answering. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI '18, pages 5981--5988, 2018a.
- Tongzhou Wang , Jun-Yan Zhu , Antonio Torralba , and Alexei A Efros . Dataset distillation. arXiv preprint arXiv:1811.10959 , 2018 b. Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. Dataset distillation. arXiv preprint arXiv:1811.10959, 2018b. · arXiv
- Jason Weston , Sumit Chopra , and Antoine Bordes . Memory networks . In Proceedings of the 3rd International Conference on Learning Representations, ICLR '15 , 2015 . Jason Weston, Sumit Chopra, and Antoine Bordes. Memory networks. In Proceedings of the 3rd International Conference on Learning Representations, ICLR '15, 2015.
- Adams Wei Yu , David Dohan , Minh-Thang Luong , Rui Zhao , Kai Chen , Mohammad Norouzi , and Quoc V Le. Qanet : Combining local convolution with global self-attention for reading comprehension . In Proceedings of the 6th International Conference on Learning Representations, ICLR '18 , 2018 . Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. Qanet: Combining local convolution with global self-attention for reading comprehension. In Proceedings of the 6th International Conference on Learning Representations, ICLR '18, 2018.
Cited by
Sorted by citations
Precise Zero-Shot Dense Retrieval without Relevance Labels
Gao, L., Ma, X., Lin, J., & Callan, J. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.99 · Cited by 362
Large Language Models for Information Retrieval: A Survey
Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Chen, H., Liu, Z., Dou, Z., & Wen, J.-R. (2026) · ACM Transactions on Information Systems · doi:10.1145/3748304 · Cited by 225
Dense Text Retrieval Based on Pretrained Language Models: A Survey
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3637870 · Cited by 211
From Matching to Generation: A Survey on Generative Information Retrieval
Li, X., Jin, J., Zhou, Y., Zhang, Y., Zhang, P., Zhu, Y., & Dou, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3722552 · Cited by 172
Information Retrieval: Recent Advances and Beyond
Hambarde, K., & Proença, H. P. M. C. (2023) · IEEE Access · doi:10.1109/access.2023.3295776 · Cited by 212
When Human-AI Interactions Become Parasocial: Agency and Anthropomorphism in Affective Design
Maeda, T., & Quan-Haase, A. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658956 · Cited by 144
In Conversation with Artificial Intelligence: Aligning language Models with Human Values
Kasirzadeh, A., & Gabriel, I. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00606-x · Cited by 127
Shah, C. S., & Bender, E. M. (2022) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3498366.3505816 · Cited by 125
Generative AI meets Responsible AI: Practical Challenges and Opportunities
Kenthapadi, K., Lakkaraju, H., & Rajani, N. F. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599557 · Cited by 126
Cheong, I., Xia, K., Feng, K. J. K., Chen, Q. Z., & Zhang, A. X. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3659048 · Cited by 91
Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?
Shah, C. S., & Bender, E. M. (2024) · ACM Transactions on the Web · doi:10.1145/3649468 · Cited by 86
GERE: Generative Evidence Retrieval for Fact Verification
Chen, J., Zhang, R., Guo, J., Fan, Y., & Cheng, X. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531827 · Cited by 66
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
Chen, J., Zhang, R., Guo, J., Liu, Y., Fan, Y., & Cheng, X. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557271 · Cited by 57
ChatGPT Hallucinates when Attributing Answers
Zuccon, G., Koopman, B., & Shaik, R. S. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625329 · Cited by 56
Pre-training Methods in Information Retrieval
Fan, Y., Xie, X., Cai, Y., Chen, J., Ma, X., Li, X., Zhang, R., & Guo, J. (2022) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000100 · Cited by 69
Murgia, E., Abbasiantaeb, Z., Aliannejadi, M., Huibers, T. W. C., Landoni, M., & Pera, M. S. (2023) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3563359.3597399 · Cited by 55
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
MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation
Qian, H., Liu, Z., Zhang, P., Mao, K., Lian, D., Dou, Z., & Huang, T. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714805 · Cited by 86
Advertising as Information for Ranking E-Commerce Search Listings
Yang, J., Sahni, N. S., Nair, H. S., & Xi, X. (2023) · Marketing Science · doi:10.1287/mksc.2021.0292 · Cited by 31
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
Kenthapadi, K., Sameki, M., & Taly, A. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671467 · Cited by 36
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Tang, Y., Zhang, R., Guo, J., Chen, J., Zhu, Z., Wang, S., Yin, D., & Cheng, X. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599903 · Cited by 29
How Does Generative Retrieval Scale to Millions of Passages?
Pradeep, R., Hui, K., Gupta, J. P., Lelkes, A. D., Zhuang, H., Lin, J., Metzler, D., & Tran, V. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.83 · Cited by 70
Self-citationNOVO: Learnable and Interpretable Document Identifiers for Model-Based IR
Wang, Z., Zhou, Y., Tu, Y. L., & Dou, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614993 · Cited by 25
TravelRAG: A Tourist Attraction Retrieval Framework Based on Multi-Layer Knowledge Graph
Song, S., Yang, C., Li, X., Shang, H., Li, Z., & Chang, Y. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13110414 · Cited by 29
Credible without Credit: Domain Experts Assess Generative Language Models
Peskoff, D., & Stewart, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.37 · Cited by 31
Li, X., Zhou, Y., & Dou, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28714 · Cited by 30
A Map of Exploring Human Interaction Patterns with LLM: Insights into Collaboration and Creativity
Li, J., Li, J., & Su, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60615-1_5 · Cited by 30
Recent Advances in Generative Information Retrieval
Tang, Y., Zhang, R., Guo, J., & de Rijke, M. (2023) · Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region · doi:10.1145/3624918.3629547 · Cited by 22
Generative Information Retrieval
Najork, M. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591871 · Cited by 22
Self-citationCitation: A Key to Building Responsible and Accountable Large Language Models
Huang, J., & Chang, K. (2024) · Findings of the Association for Computational Linguistics: NAACL 2024 · doi:10.18653/v1/2024.findings-naacl.31 · Cited by 72
DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index
Zhou, Y., Yao, J., Dou, Z., Wu, L., & Wen, J.-R. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1373-9 · Cited by 39
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks
Li, X., Dou, Z., Zhou, Y., & Liu, F. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657778 · Cited by 24
Generative Retrieval via Term Set Generation
Zhang, P., Liu, Z., Zhou, Y., Dou, Z., Liu, F., & Cao, Z. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657797 · Cited by 38
Sociotechnical Implications of Generative Artificial Intelligence for Information Access
Mitra, B., Cramer, H. S. M., & Gurevich, O. (2024) · The information retrieval series · doi:10.1007/978-3-031-73147-1_7 · Cited by 28
Chatbots, search engines, and the sealing of knowledges
Lindemann, N. F. (2024) · AI & Society · doi:10.1007/s00146-024-01944-w · Cited by 16
In conversation with Artificial Intelligence: aligning language models with human values
Kasirzadeh, A., & Gabriel, I. (2022) · arXiv · doi:10.48550/arxiv.2209.00731 · Cited by 128
Advances in artificial intelligence: a review for the creative industries
Anantrasirichai, N., Zhang, F., & Bull, D. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11494-w · Cited by 62
Mayerhofer, K., Capra, R., & Elsweiler, D. (2025) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3698204.3716454 · Cited by 26
Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback
Zhou, Y., Dou, Z., & Wen, J.-R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.768 · Cited by 18
Wang, H., & Na, T. (2023) · ACM SIGIR Forum · doi:10.1145/3642979.3643007 · Cited by 14
Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval
Bénédict, G., Zhang, R., & Metzler, D. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591923 · Cited by 14
Self-citationListwise Generative Retrieval Models via a Sequential Learning Process
Tang, Y., Zhang, R., Guo, J., de Rijke, M., Chen, W., & Cheng, X. (2024) · ACM Transactions on Information Systems · doi:10.1145/3653712 · Cited by 18
Large Search Model: Redefining Search Stack in the Era of LLMs
Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., & Wei, F. (2023) · ACM SIGIR Forum · doi:10.1145/3642979.3643006 · Cited by 19
The Role of Complex NLP in Transformers for Text Ranking
Rau, D., & Kamps, J. (2022) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3539813.3545144 · Cited by 14
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse PromptsPreprint
Newman, B., Choubey, P. K., & Rajani, N. F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07280 · Cited by 17
Report on the Search Futures Workshop at ECIR 2024
Azzopardi, L. A., Clarke, C. L. A., Kantor, P. B., Mitra, B., Trippas, J. R., Ren, Z., Aliannejadi, M., Arabzadeh, N., Chandrasekar, R., de Rijke, M., et al. (2024) · ACM SIGIR Forum · doi:10.1145/3687273.3687288 · Cited by 14
Yamamoto, Y. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1382234 · Cited by 12
Blind Faith? User Preference and Expert Assessment of AI-Generated Religious Content
Alam, S., Abdulhai, M., & Salehi, N. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732162 · Cited by 15
Large Language Models for Information Retrieval: Challenges and Chances
Breuer, T., Frihat, S., Fuhr, N., Lewandowski, D., Schaer, P., & Schenkel, R. (2025) · Datenbank-Spektrum · doi:10.1007/s13222-025-00503-x · Cited by 19
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_2 · Cited by 12
Control search rankings, control the world: what is a good search engine?
Coghlan, S., Chia, H. X., Scholer, F., & Spina, D. (2025) · AI and Ethics · doi:10.1007/s43681-025-00695-8 · Cited by 7
Robust Neural Information Retrieval: An Adversarial and Out-of-Distribution Perspective
Liu, Y.-A., Zhang, R., Guo, J., de Rijke, M., Fan, Y., & Cheng, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3768153 · Cited by 25
Li, R., Wang, Y., Wen, Z., Cui, M., & Miao, Q. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112789 · Cited by 10
An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering
Li, M., Ma, X., & Lin, J. (2022) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2022.trustnlp-1.1 · Cited by 7
Fabre, R., Azeroual, O., Bellot, P., Schöpfel, J., & Egret, D. (2022) · Future Internet · doi:10.3390/fi14090262 · Cited by 7
GLEN: Generative Retrieval via Lexical Index Learning
Lee, S., Choi, M., & Lee, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.477 · Cited by 10
Conversational Argument Search Under Selective Exposure: Strategies for Balanced Perspective Access
Kim, K., Ryu, J., Heo, D., Song, H., Oh, C., & Suh, B. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730175 · Cited by 6
Generative Retrieval for Book Search
Tang, Y., Zhang, R., Guo, J., de Rijke, M., Liu, S., Wang, S., Yin, D., & Cheng, X. (2025) · Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 · doi:10.1145/3690624.3709435 · Cited by 5
WebUltron: An Ultimate Retriever on Webpages Under the Model-Centric Paradigm
Zhou, Y., Yao, J., Wu, L., Dou, Z., & Wen, J.-R. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3332858 · Cited by 6
Pardi, G., Gottschling, S., Gerjets, P. H., & Kammerer, Y. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100126 · Cited by 6
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
On the Robustness of Generative Information Retrieval Models: An Out-of-Distribution Perspective
Liu, Y.-A., Zhang, R., Guo, J., Zhou, C., de Rijke, M., & Cheng, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88711-6_26 · Cited by 12
CiteME: Can Language Models Accurately Cite Scientific Claims?
Press, O., Hochlehnert, A., Prabhu, A., Udandarao, V., Press, O., & Bethge, M. (2024) · arXiv · doi:10.48550/arxiv.2407.12861 · Cited by 4
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3
“A new paradigm has recently been added to this palette, generative information retrieval (GenIR) [34, 52].”
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Wu, S., Ren, Z., Xin, X., Yang, J., Zhang, M., Chen, Z., de Rijke, M., & Ren, P. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729934 · Cited by 10
Bautista, L. R. P., & Vasquez, R. C. (2026) · Investigación Bibliotecológica Archivonomía Bibliotecología e Información · doi:10.22201/iibi.24488321xe.2025.105.59051 · Cited by 6
1-PAGER: One Pass Answer Generation and Evidence Retrieval
Jain, P., Soares, L. B., & Kwiatkowski, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.967 · Cited by 5
Wang, Q., & Liu, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128919 · Cited by 4
Recent Advances in Generative Information Retrieval
Tang, Y., Zhang, R., Sun, W., Guo, J., & de Rijke, M. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3641239 · Cited by 3
Mordo, T., Tennenholtz, M., & Kurland, O. (2024) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3664190.3672517 · Cited by 3
Boosting Retrieval-Augmented Generation with Generation-Augmented Retrieval: A Co-Training Approach
Tang, Y., Zhang, R., Guo, J., de Rijke, M., Fan, Y., & Cheng, X. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729907 · Cited by 3
Replication and Exploration of Generative Retrieval over Dynamic Corpora
Zhang, Z., Ma, X., Sun, W., Ren, P., Chen, Z., Wang, S., Yin, D., de Rijke, M., & Ren, Z. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730314 · Cited by 2
Understanding and Applying Deep Learning
Lippmann, R. P. (2022) · Neural Computation · doi:10.1162/neco_a_01518 · Cited by 5
DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding
Qiao, S., Liu, X., & Na, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.638 · Cited by 3
Recent Advances in Generative Information Retrieval
Tang, Y., Zhang, R., Ren, Z., Guo, J., & de Rijke, M. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-56069-9_48 · Cited by 2
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Guo, J., Zhou, C., Zhang, R., Chen, J., de Rijke, M., Fan, Y., & Cheng, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3763233 · Cited by 10
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book QA
Erbacher, P., Falissard, L., Guigue, V., & Soulier, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56063-7_31 · Cited by 4
Unifying Corroborative and Contributive Attributions in Large Language Models
Worledge, T., Shen, J. H., Meister, N., Winston, C., & Guestrin, C. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00039 · Cited by 14
AI to publish knowledge: a tectonic shift
Lemberger, T. (2024) · EMBO Reports · doi:10.1038/s44319-024-00119-4 · Cited by 3
One-step Reach: LLM-based Keyword Generation for Sponsored Search Advertising
Wang, Y., Sha, Z., Lin, K., Feng, C., Zhu, K., Wang, L., Jiao, X., Huang, F., Ye, C., He, D., et al. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3651943 · Cited by 1
Designing for the Future of Information Access with Generative Information Retrieval
Murdock, V., Lee, C., & Hersh, W. (2024) · The information retrieval series · doi:10.1007/978-3-031-73147-1_9 · Cited by 2
Generative AI for content discovery in academic publishing: A content provider’s perspective
Zhu, J. (2025) · Information Services & Use · doi:10.1177/18758789251336075 · Cited by 2
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
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 4
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 3
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
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
Self-citationIntellectNavigator: Enhancing Search Tools with LLMs-Powered Query Instruction
Vo, D. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3665843 · Cited by 2
An Open Source Direct Messaging and Enhanced Recommendation System for Yioop
Mallya, A. D. (2021) · doi:10.31979/etd.474u-r5rn · Cited by 1
Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842
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
Martens, B. V. D. V. (2023) · Synthesis lectures on information concepts, retrieval, and services · doi:10.1007/978-3-031-32530-4_9
Negotiating becoming: a Nietzschean critique of large language models
Fischer, S. W. S., & de Boer, B. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09783-5 · Cited by 1
The Evolution of Information Seeking Practices and Technologies and the Future of the Library
Wan, S. Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95689-5.00238-8
Generative Insights Unveiling AI's Evolution and Algorithms
Elavarasi, D., Ramadevi, M., Jayabarathan, J. K., & Robinson, S. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9173-0.ch001
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 · Cited by 1
White, R. W., & Shah, C. S. (2024) · The information retrieval series · doi:10.1007/978-3-031-73147-1_1
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
Metadata
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
