Retrieval-based Language Models and Applications
Open accessAbstract
Retrieval-based language models (LMs) have shown impressive performance on diverse NLP tasks. In this tutorial, we will provide a comprehensive and coherent overview of recent advances in retrieval-based LMs. We will start by providing preliminaries covering the foundation of LMs (e.g., masked LMs, autoregressive LMs) and retrieval systems (e.g., nearest-neighbor search). We will then detail recent progress in retrieval-based models, focusing on their model architectures and learning approaches. Finally, we will show how retrieval-based LMs are adapted to downstream applications, and extended to multilingual and multi-modal settings. Finally, we will use an exercise to showcase the effectiveness of retrieval-based LMs.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
After ChatGPT demonstrated superior text generation at the end of 2022, there have been many studies and surveys on RAG and its applications in LLMs.
“After ChatGPT [1] demonstrated superior ability text generation at the end of 2022, there have been many studies and surveys on RAG and its applications in LLMs [84, 90, 91].”
From Foundations of GenIR (2025) · cites this paper as [91]
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575 · see citing works
Self-citationRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works
How Much Knowledge Can You Pack into the Parameters of a Language Model?
Roberts, A., Raffel, C., & Shazeer, N. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.437 · Cited by 638 · see citing works
Retrieval Augmentation Reduces Hallucination in Conversation
Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.320 · Cited by 575 · see citing works
Improving language models by retrieving from trillions of tokens
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. V. D., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04426 · Cited by 382 · see citing works
Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.546 · Cited by 340 · see citing works
Self-citationGeneralization through Memorization: Nearest Neighbor Language Models
Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2019) · arXiv · doi:10.48550/arxiv.1911.00172 · Cited by 285 · see citing works
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022) · arXiv · doi:10.48550/arxiv.2208.03299 · Cited by 213 · see citing works
Mallen, A. T., Asai, A., Zhong, V., Das, R., Hajishirzi, H., & Khashabi, D. (2022) · arXiv.org · doi:10.48550/arxiv.2212.10511 · Cited by 212 · see citing works
Self-citationEditing Factual Knowledge in Language Models
De Cao, N., Aziz, W., & Titov, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.522 · Cited by 180 · see citing works
Unsupervised Dense Information Retrieval with Contrastive Learning
Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., & Grave, E. (2021) · arXiv · doi:10.48550/arxiv.2112.09118 · Cited by 172 · see citing works
Nearest Neighbor Machine Translation
Khandelwal, U., Fan, A., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2020) · arXiv · doi:10.48550/arxiv.2010.00710 · Cited by 165 · see citing works
Large Language Models Struggle to Learn Long-Tail Knowledge
Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2211.08411 · Cited by 143 · see citing works
Retrieval Augmented Code Generation and Summarization
Parvez, M. R., Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.232 · Cited by 141 · see citing works
Entities as Experts: Sparse Memory Access with Entity Supervision
Févry, T., Soares, L. B., FitzGerald, N., Choi, E., & Kwiatkowski, T. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.400 · Cited by 138 · see citing works
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Chen, W., Hu, H., Chen, X., Verga, P., & Cohen, W. W. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.375 · Cited by 134 · see citing works
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., d'Autume, C. D. M., Kocisky, T., Ruder, S., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.01951 · Cited by 90 · see citing works
Recent Advances in Retrieval-Augmented Text Generation
Cai, D., Wang, Y., Liu, L., & Shi, S. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532682 · Cited by 79 · see citing works
Adaptive Nearest Neighbor Machine Translation
Zheng, X. W., Zhang, Z., Guo, J., Huang, S., Chen, B., Luo, W., & Chen, J. (2021) · doi:10.18653/v1/2021.acl-short.47 · Cited by 74 · see citing works
Open-Domain Question Answering
Chen, D., & Yih, W.-T. (2020) · doi:10.18653/v1/2020.acl-tutorials.8 · Cited by 67 · see citing works
Self-citationTraining language models with memory augmentation
Zhong, Z., Lei, T., & Chen, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.382 · Cited by 60 · see citing works
Self-citationEfficient Nearest Neighbor Language Models
He, J., Neubig, G., & Berg-Kirkpatrick, T. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.461 · Cited by 59 · see citing works
Adaptive Semiparametric Language Models
Yogatama, D., d’Autume, C. D. M., & Kong, L. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00371 · Cited by 58 · see citing works
Teaching language models to support answers with verified quotes
Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., Chadwick, M., Glaese, M., Young, S., Campbell-Gillingham, L., Irving, G., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.11147 · Cited by 53 · cited 2× · see citing works
“…without retrieval by a large margin with much fewer parameters (Mallen et al., 2022), can update their knowledge by replacing their retrieval corpora (Izacard et al., 2022b), and provide citations for users to easily verify and evaluate the predictions (Menick et al., 2022; Bohnet et al., 2022).”
BackgroundRealTime QA: What's the Answer Right Now?
Kasai, J., Sakaguchi, K., Takahashi, Y., Bras, R. L., Asai, A., Yu, X., Radev, D., Smith, N., Choi, Y., & Inui, K. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2130 · Cited by 52 · see citing works
Self-citationRealTime QA: What's the Answer Right Now?
Kasai, J., Sakaguchi, K., Takahashi, Y., Bras, R. L., Asai, A., Yu, X., Radev, D., Smith, N. A., Choi, Y., & Inui, K. (2022) · arXiv · doi:10.48550/arxiv.2207.13332 · Cited by 52 · see citing works
Self-citationTask-aware Retrieval with Instructions
Asai, A., Schick, T., Lewis, P., Chen, X., Izacard, G., Riedel, S., Hajishirzi, H., & Yih, W.-T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.225 · Cited by 46 · see citing works
Self-citationRetrieval-Augmented Multimodal Language ModelingPreprint
Yasunaga, M., Aghajanyan, A., Shi, W., James, R., Leskovec, J., Liang, P., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.12561 · Cited by 37 · see citing works
One Question Answering Model for Many Languages with Cross-lingual Dense Passage RetrievalPreprint
Asai, A., Yu, X., Kasai, J., & Hajishirzi, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11976 · Cited by 36 · see citing works
Self-citationNeuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Alon, U., Xu, F. F., He, J., Sengupta, S., Roth, D., & Neubig, G. (2022) · arXiv · doi:10.48550/arxiv.2201.12431 · Cited by 32 · see citing works
Controllable Semantic Parsing via Retrieval Augmentation
Pasupat, P., Zhang, Y., & Guu, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.607 · Cited by 30 · see citing works
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., Soares, L. B., Ciaramita, M., Eisenstein, J., Ganchev, K., Herzig, J., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.08037 · Cited by 30 · see citing works
Nonparametric Masked Language Modeling
Min, S., Shi, W., Lewis, M., Chen, X., Yih, W.-T., Hajishirzi, H., & Zettlemoyer, L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.132 · Cited by 29 · see citing works
Self-citationNearest Neighbor Zero-Shot Inference
Shi, W., Michael, J., Gururangan, S., & Zettlemoyer, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.214 · Cited by 28 · see citing works
Mention Memory: incorporating textual knowledge into Transformers through entity mention attentionPreprint
de Jong, M., Zemlyanskiy, Y., FitzGerald, N., Sha, F., & Cohen, W. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06176 · Cited by 20 · see citing works
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Wang, Z., Pan, X., Yu, D., Yu, D., Chen, J., & Ji, H. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.246 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., & Li, Q. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671470 · Cited by 827 · see citing works
Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876 · see citing works
“Self-RAG [Asai et al. , 2023] introduces “reflection to-kens” that allow the model to introspect its outputs.”
BackgroundMethodInfluentialRetrieval-Augmented Generation for AI-Generated Content: A Survey
Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., Yang, L., Zhang, W., Jiang, J., & Cui, B. (2026) · Data Science and Engineering · doi:10.1007/s41019-025-00335-5 · Cited by 549 · see citing works
Survey of vector database management systems
Pan, J. J., Wang, J., & Li, G. (2024) · The VLDB Journal · doi:10.1007/s00778-024-00864-x · Cited by 221 · see citing works
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Wu, J., Yang, S., Zhan, R., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Computational Linguistics · doi:10.1162/coli_a_00549 · Cited by 212 · see citing works
An LLM-based approach for enabling seamless Human-Robot collaboration in assembly
Gkournelos, C., Konstantinou, C., & Makris, S. (2024) · CIRP Annals · doi:10.1016/j.cirp.2024.04.002 · Cited by 133 · see citing works
CLIP in medical imaging: A survey
Zhao, Z., Liu, Y., Wu, H., Wang, M., Li, Y., Wang, S., Teng, L., Liu, D., Cui, Z., Wang, Q., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103551 · Cited by 175 · see citing works
Policy advice and best practices on bias and fairness in AI
Álvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I. T., Lobo, P. R., et al. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09746-w · Cited by 128 · see citing works
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
Xu, M., Niyato, D. T., Kang, J., Xiong, Z., Mao, S., Han, Z., Kim, D. I., & Letaief, K. B. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.005.2400019 · Cited by 110 · see citing works
Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.11511 · Cited by 101 · see citing works
“Retrieval-Augmented Generation (RAG) methods (Figure 1 left; Lewis et al. 2020; Guu et al. 2020) augment the input of LLMs with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks (Ram et al., 2023; Asai et al., 2023a).”
MethodSelf-citationFine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
Soudani, H., Kanoulas, E., & Hasibi, F. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3673791.3698415 · Cited by 102 · see citing works
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2024) · doi:10.2139/ssrn.4809363 · Cited by 69 · see citing works
Vector Database Management Techniques and Systems
Pan, J. J., Wang, J., & Li, G. (2024) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3626246.3654691 · Cited by 57 · see citing works
When Large Language Models Meet Vector Databases: A Survey
Jing, Z., Su, Y., & Han, Y. (2025) · 2025 Conference on Artificial Intelligence x Multimedia (AIxMM) · doi:10.1109/aixmm62960.2025.00008 · Cited by 93 · see citing works
Vector Search with OpenAI Embeddings: Lucene Is All You Need
Xian, J., Teofili, T., Pradeep, R., & Lin, J. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635691 · Cited by 35 · see citing works
Generating TRIZ-inspired guidelines for eco-design using Generative Artificial Intelligence
Lee, C. K. M., Liang, J., Yung, K. L., & Keung, K. L. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102846 · Cited by 34 · see citing works
Evaluating search engines and large language models for answering health questions
Fernández-Pichel, M., Pichel, J. C., & Losada, D. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01546-w · Cited by 33 · see citing works
Synthesizing scientific literature with retrieval-augmented language models
Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D’Arcy, M., et al. (2026) · Nature · doi:10.1038/s41586-025-10072-4 · Cited by 38 · see citing works
Self-citationUniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Wei, C., Chen, Y., Chen, H., Hu, H., Zhang, G., Fu, J., Ritter, A., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73021-4_23 · Cited by 45 · see citing works
Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation
Horita, D., Inoue, N., Kikuchi, K., Yamaguchi, K., & Aizawa, K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00015 · Cited by 26 · see citing works
Retrieval-Augmented Egocentric Video Captioning
Xu, J., Huang, Y., Hou, J., Chen, G., Zhang, Y., Feng, R., & Xie, W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01284 · Cited by 24 · see citing works
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents
Tanaka, R., Iki, T., Hasegawa, T., Nishida, K., Saito, K., & Suzuki, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02312 · Cited by 29 · see citing works
Gou, Y., Gao, J., Xu, Y., & Long, C. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709730 · Cited by 26 · see citing works
Privacy Implications of Retrieval-Based Language Models
Huang, Y., Gupta, S., Zhong, Z., Li, K., & Chen, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.921 · Cited by 27 · see citing works
Self-citationRoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search
Chen, M., Zhang, K., He, Z., Jing, Y., & Wang, X. S. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681959 · Cited by 19 · see citing works
KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Choi, S.-H., Fang, T., Wang, Z., & Song, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.867 · Cited by 25 · see citing works
Dynamic and Parametric Retrieval-Augmented Generation
Su, W., Ai, Q., Zhan, J., Dong, Q., & Liu, Y. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731692 · Cited by 10 · see citing works
RIGHT: Retrieval-Augmented Generation for Mainstream Hashtag Recommendation
Fan, R., Fan, Y., Chen, J., Guo, J., Zhang, R., & Cheng, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56027-9_3 · Cited by 10 · see citing works
Filtered Vector Search: State-of-the-Art and Research Opportunities
Chronis, Y., Caminal, H., Yannis Papakonstantinou, D., Özcan, F., & Ailamaki, A. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3750601.3750700 · Cited by 9 · see citing works
Egocentric and exocentric methods: A short survey
Thatipelli, A., Lo, S.-Y., & Roy–Chowdhury, A. K. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104371 · Cited by 8 · see citing works
Tax Intelligent Decision-Making Language Model
Zhong, Y., Wong, D., & Lan, K. (2024) · IEEE Access · doi:10.1109/access.2024.3419079 · Cited by 7 · see citing works
Select Edges Wisely: Monotonic Path Aware Graph Layout Optimization for Disk-Based ANN Search
Yue, Z., Zheng, B., Xu, L., Xu, K., Zhang, S., Du, Y., Gao, Y., Zhou, X., & Jensen, C. S. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749697 · Cited by 8 · see citing works
Learning to Supervise Knowledge Retrieval Over a Tree Structure for Visual Question Answering
Xu, N., Lu, Z., Tian, H., Kang, R., Cao, J., Zhang, Y., & Liu, A.-A. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3355638 · Cited by 6 · see citing works
Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmentation
Luo, Z., Wang, Y., Ke, W., Qi, R., Guo, Y., & Wang, P. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446860 · Cited by 6 · see citing works
Cracking Vector Search Indexes
Mageirakos, V., Wu, B., & Alonso, G. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749666 · Cited by 8 · see citing works
Han, L., Pan, S., Xing, Z., Sun, J., Yitagesu, S., Zhang, X., & Feng, Z. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3523284 · Cited by 5 · see citing works
Timestamp Approximate Nearest Neighbor Search Over High-Dimensional Vector Data
Wang, Y.-X., He, Z., Tong, Y., Zhou, Z., & Zhong, Y. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00228 · Cited by 4 · see citing works
Maximum Inner Product is Query-Scaled Nearest Neighbor
Chen, T., Fu, C., Wang, K., Ke, X., Gao, Y., Zhou, W., Ni, Y., & Zeng, A. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3725688.3725705 · Cited by 3 · see citing works
Ma, J., Zhou, Y., He, L., Zhang, Q., Bilal, M. A., & Zhang, Y. (2025) · Minerals · doi:10.3390/min15101023 · Cited by 4 · see citing works
Process-Oriented Dual-Layer Knowledge GraphRAG for Reservoir Engineering Decision Support
Jiang, B., Liu, Z., Wang, N., Li, Z., Shi, Y., & Lin, B. (2025) · Processes · doi:10.3390/pr13103230 · Cited by 3 · see citing works
Constructing Set-Compositional and Negated Representations for First-Stage Ranking
Krasakis, A. M., Yates, A., & Kanoulas, E. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761238 · Cited by 6 · see citing works
Efficient Feature Extraction for Image Analysis through Adaptive Caching in Vector Databases
Monir, S. S., & Zhao, D. (2024) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict62343.2024.00036 · Cited by 3 · see citing works
Identifying symptom etiologies using syntactic patterns and large language models
Taub-Tabib, H., Shamay, Y., Shlain, M., Pinhasov, M., Polak, M., Tiktinsky, A., Rahamimov, S., Bareket, D., Eyal, B., Kassis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-65645-6 · Cited by 3 · see citing works
Efficient and Tractable Conflict Detection in Knowledge Graphs via Path Analysis
Wang, Y., Song, Q., Zheng, L., & Li, X. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3575948 · Cited by 3 · see citing works
L3X: Long Object List Extraction from Long Documents
Singhania, S., Razniewski, S., & Weikum, G. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761460 · Cited by 2 · see citing works
HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
Santra, P., Ghosh, M., Ganguly, D., Basuchowdhuri, P., & Naskar, S. K. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760942 · Cited by 2 · see citing works
Retrieval Augmented Generation (RAG) using LLMs
Vahaj, M., Raza, S. M., & Nehra, V. (2025) · Annual International Conference on Data Science, Machine Learning and Blockchain Technology (AICDMB) · doi:10.1109/aicdmb64359.2025.11277692 · Cited by 2 · see citing works
Roy, K., Zi, Y., Shyalika, C., Prasad, R., Murali, S., Palit, V., & Sheth, A. (2024) · Preprints.org · doi:10.20944/preprints202407.0376.v1 · Cited by 2 · see citing works
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models
Hong, X., Shao, Y., Wang, Z., Duan, M., & Jin, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9431-7_18 · Cited by 1 · see citing works
Bansal, S., Parimala, S., & Kumar, N. (2025) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v19i1.35812 · Cited by 1 · see citing works
Towards Accurate Distance Estimation for Distribution-Aware c-ANN Search
Deng, L., Chen, P., Zeng, X., Fang, Y., Chen, J., & Zhao, Y. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00180 · Cited by 1 · see citing works
ReCompGPT: An NL2NL Framework for Automated Requirements Completeness
Sheng, T., Wang, L., & Liu, H. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3613507
GADE+: A Graph-Based Anchor-Enhanced Framework for Targeted Document Detection
Wen, H., Shen, W., & Li, G. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3621835 · Cited by 1 · see citing works
Jin, L., Zhao, C., Han, D., Zuo, Z., Liang, Y., Liao, Y., & Tohti, T. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02676-z · Cited by 2 · see citing works
Memento: Towards Proactive Visualization of Everyday Memories with Personal Wearable AR Assistant
Kim, Y., Yang, Y., & Kaufman, A. E. (2026) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw70859.2026.00180 · Cited by 1 · see citing works
Retrieval-Augmented Generation (RAG): Architecture, Ethical Challenges, and Optimization Strategies
Gade, S. R., Ponna, D., Shah, J., & Patel, K. A. (2026) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas67792.2026.11483907 · Cited by 1 · see citing works
Ambiguous Entity Oriented Targeted Document Detection
Shen, W., & Wen, H. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00072 · Cited by 1 · see citing works
TEMDG: A Multi-Round Dialogue Generation Model Incorporating Topic and Historical Information
Yin, L., Liu, X., Li, P., & Gu, H. (2024) · IEEE Access · doi:10.1109/access.2024.3492009 · Cited by 1 · see citing works
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Chen, T., Fu, C., Ke, X., Gao, Y., Ni, Y., & Zeng, A. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730088 · Cited by 1 · see citing works
FedVS: Towards Federated Vector Similarity Search with Filters
Fan, Z., Zeng, Y., Zheng, Z., Yang, B., & Tong, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736958 · Cited by 2 · see citing works
Exploring Optimal Prefetching Sizes in RaLMSpec to Enhance Retrieval-Augmented Generation Efficiency
Liu, S. (2025) · Highlights in Science, Engineering and Technology · doi:10.54097/hhff9g78
Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842
“After ChatGPT [1] demonstrated superior ability text generation at the end of 2022, there have been many studies and surveys on RAG and its applications in LLMs [84, 90, 91].”
Towards an FA ChatBot with Retrieval-Augmented Language Modeling
Fichtenkamm, M., Kofler, M., Schekotihin, K., & Burmer, C. (2024) · IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) · doi:10.1109/ipfa61654.2024.10691083
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
A Large Language Model-Based Motion Planning for Human-Robot Interaction: An Experimental Case Study
Coppari, A., Proia, S., Ruo, A., Favali, F., Sabattini, L., Secchi, C., Villani, V., Piazzola, M., & Capra, L. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-81688-8_8
SReLLM, Strategic Retrieval Enhanced Large Language Model for Fake News Detector
Mishra, A. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i39s.7455
COTKEG: A Chain-of-Thought and Knowledge Graph Enhanced Method for Medical Large Language Models
Huang, J., & Yan, E.-L. (2025) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1109/iscait64916.2025.11010691
Yu, X., Cai, C., Feng, Z., Lin, H., Xu, S., & Peng, Y. (2025) · International Symposium on Autonomous Decentralized Systems · doi:10.1109/isads66912.2025.00014
Rethinking Data Use in Large Language Models
Min, S., Hajishirzi, H., & Zettlemoyer, L. (2025) · Computational Linguistics · doi:10.1162/coli.a.573
Self-citationContext-Aware Fine-Grained Graph RAG for Query-Focused Summarization
Hong, Y., Li, C., Zhang, J., & Shao, Y. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760935
FRAG: Focused Retrieval Augmented Generation Reducing Retrieval Scope by Mapping Table
Chen, S., & Kong, F. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_12
Lu, H., & Zhao, Z. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227585
High-Precision Multimodal Retrieval-Augmented Generation
Wang, P., Yang, J., & Yu, T. (2025) · International Conference on Advanced Algorithms and Neural Networks (AANN) · doi:10.1109/aann66429.2025.11257632
Mixture of Cluster-Guided Experts for Retrieval-Augmented Label Placement
Zhang, P., Che, E., Chen, Y. J., Huang, B., Ling, H., & Qu, J. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3642518
DTCR: Enhancing Unsupervised Sentence Representations via Dual-Track Cognitive Reasoning
Feng, J., Tong, H., Lu, J., & Liu, X. (2025) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn69125.2025.00026
Retrieval-Augmented Generation
Yu, J. S., & Yao, Y. (2026) · Springer eBooks · doi:10.1007/978-981-95-4632-9_4
RED-ANNS: An RDMA-Enabled Distributed Framework for Graph-Based Approximate Nearest Neighbor Search
Chen, Y., Zhang, K., Chen, S., Xiao, S., Zou, X., Ren, R., Jing, Y., Wang, X. S., Cao, L., & Wan, M. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3778092.3778101
Liu, G. K.-M., Li, B., Cohan, A., Walden, W. G., & Yang, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21300-6_26
Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks
Dai, T., Zhang, C., & Zou, J. (2026) · Jisuanji shenghuojia. · doi:10.54097/8jvwpk07
GPS: Revisiting the Data Layout for Disk-based High-Dimensional Vector Search
Yin, P., Yan, X., Zhou, Q., Li, H., Li, X., Wang, M., Zhang, L., Yao, X., & Cheng, J. S.-C. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3802069
Approximate Nearest Neighbor Search Over Temporal Vector Data
Wang, Y., He, Z., Tong, Y., Zhou, Z., Ma, Z., & Zhong, Y. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3722749
SQLVec: SQL-Based Vector Similarity Search
张喆群, Zhu, Y., Zhang, H., & Yu, J. X. (2026) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65706.2026.00246
Xu, Y., Shen, Y., Chen, Y., & Huang, L. (2026) · Proceedings of the VLDB Endowment · doi:10.14778/3828612.3828642
- Authors
- Asai, A., Min, S., Zhong, Z., & Chen, D.
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)
- Published
- 2023
- Pages
- 41-46
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385570290
- Cited by
- 81
- References
- 37
- Citations by year
- 2026: 12 · 2025: 39 · 2024: 28 · 2023: 2
- FWCI
- 14.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- language models, retrieval systems, nearest neighbor search, masked language models, autoregressive language models, model architecture, multilingual language models, multimodal language models, downstream applications
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
