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REPLUG: Retrieval-Augmented Black-Box Language Models
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Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
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“However, most RAG systems still retrieve unstructured chunks and rely on one-shot generation, which often yields redundant context, low information density, and brittle multi-hop reasoning.”
From TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation (2026) · cites this paper as [19]
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“This reduces the computational burden of finetuning large models (e.g., 405B parameters) by minimizing the KL divergence between retrieval likelihood and the model's output perplexity.”
From The AI Hippocampus: How Far are We From Human Memory? (2026) · cites this paper as Shi et al., 2024
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Wang, Y., Guo, Q., Ni, X., Shi, C., Liu, L., Jiang, H., & Yang, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447527 · Cited by 9
CADLRA: A multi-charge prediction method based on the Criminal Act-Driven Law Retrieval Augmentation
Feng, J., Zhao, L., Qin, H., Yiming, X., & Wang, Z. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108619 · Cited by 7
Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation
Sanguigni, F., Morelli, D., Cornia, M., & Cucchiara, R. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229186 · Cited by 4
An Agent-Based RAG Architecture for Intelligent Tourism Assistance: The Valencia Case Study
Bonetti, A., Salcedo-Puche, A., Vila‐Francés, J., García, X. B., Fernández-Vargas, E., Magdalena‐Benedito, R., & Soria‐Olivas, E. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6050266 · Cited by 5
Variational Dehallucination via Retrieval-Augmented Prompt Learning
Lin, H.-Y., Chang, Y.-C., & Chien, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3646477 · Cited by 4
Shen, T., Wang, H., Qin, C., Sun, R., Song, Y., Lian, D., Zhu, H., & Chen, E. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020052 · Cited by 10
Carbon Price Forecasting with LLM-Based Refinement and Transfer-Learning
Jiang, H., Ding, Y., Chen, R., & Fan, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72356-8_10 · Cited by 5
Retrieval-Augmented Multi-Modal Chain-of-Thoughts Reasoning for Large Language Models
Liu, B., Lyu, C., Min, Z., Wang, Z., Su, J., & Wang, L. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228391 · Cited by 4
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Wu, D., Wang, H., Yu, W., Zhang, Y., Chang, K.-W., & Yu, D. (2024) · arXiv · doi:10.48550/arxiv.2410.10813 · Cited by 493
PRESS: Defending Privacy in Retrieval-Augmented Generation via Embedding Space Shifting
He, J., Liu, C., Hou, G., Jiang, W., & Li, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887843 · Cited by 4
Asking Questions to Alleviate Object Hallucination in Large Vision-Language Models
Bi, C., Dang, T., Wang, S., Cao, F., & Huang, Q. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3618949 · Cited by 3
Structured reflective reasoning for precise medical knowledge graph retrieval augmented generation
Wang, B., Wu, J., Shi, Y., Chen, W., & Gong, F. (2025) · Health Information Science and Systems · doi:10.1007/s13755-025-00390-2 · Cited by 4
Less LLM, More Documents: Searching for Improved RAG
Ning, J., Kong, Y., Long, Y., & Callan, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21289-4_38 · Cited by 9
Park, S.-I., & Lee, J.-Y. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00724 · Cited by 9
Summary report auto-generation based on hierarchical corpus using large language model
Guan, X., Ye, Z., Cao, B., & Fan, J. (2025) · Displays · doi:10.1016/j.displa.2025.103055 · Cited by 3
Santra, P., Ghosh, M., Ganguly, D., Basuchowdhuri, P., & Naskar, S. K. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70021 · Cited by 5
An Effective Retrieval Method to Improve RAG Performance
Mengmeng, S., Zhibin, L., Qingwei, W., Man, H., & Feiyang, X. (2024) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit61374.2024.10881380 · Cited by 7
CyGPT: Knowledge Graph-Based Enhancement Techniques for Large Language Models in Cybersecurity
Ou, L., Ni, X., Wu, W., & Tian, Z. (2024) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc63484.2024.00036 · Cited by 3
Retrieval-augmented visual parcel invoice understanding transformer for address correction
Jeong, Y.-B., Seo, H.-I., Kim, Y., & Kim, W.-Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111542 · Cited by 2
Guo, T., Wang, C., Liu, Y., Tang, J., Li, P., Sai, X., Yang, Q., Gao, X., Li, Z., & Wen, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130931 · Cited by 4
Guo, S., Liu, H., Chen, X., Xie, Y., Zhang, L., Han, T., Chen, H., Chang, Y., & Wang, Y.-X. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737254 · Cited by 3
DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
Gao, X., Meng, X., Dong, Y., Li, Z., & Guo, S. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3765103 · Cited by 4
PEACE: Towards Efficient Project-Level Efficiency Optimization via Hybrid Code Editing
Ren, X., Wan, J., Peng, Y. F., Liu, Z., Liang, M., Chen, D., Jiang, W., & Li, Y. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00153 · Cited by 2
RADDI: A Retrieval Augmented Framework for Drug-Drug Interaction Prediction
Zhang, H., Wang, Y., Gao, X., & Xiong, Y. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020059 · Cited by 2
Rescuing the Unpoisoned: Efficient Defense Against Knowledge Corruption Attacks on RAG Systems
Kim, M., Lee, H., & Koo, H. (2025) · Annual Computer Security Applications Conference · doi:10.1109/acsac67867.2025.00093 · Cited by 12
LURE-RAG: Lightweight Utility-Driven Reranking for Efficient RAG
Chandra, M., Ganguly, D., & Ounis, I. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21289-4_36 · Cited by 2
Li, R., Cao, S., Liu, R., & Duprey, A. (2026) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3805636 · Cited by 4
Varma, S., Shivam, S., Natarajan, S., Banerjee, A., & Roy, S. (2025) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp64353.2025.00016 · Cited by 2
From Zero to Sixty at the Speed of RAG: Improving YAML Recipe Generation via Retrieval
Farmahinifarahani, F., Babkin, P., Alamir, S., & Liu, X. (2025) · International Workshop on Large Language Models for Code (LLM4Code) · doi:10.1109/llm4code66737.2025.00011 · Cited by 2
Sakhovskiy, A., & Tutubalina, E. V. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729901 · Cited by 2
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation
Li, S., & Ramakrishnan, N. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744590 · Cited by 5
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
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Huang, Z., Li, J., Jin, L. H., Wang, J., Tu, M., Yin, H., Liu, Z., Meng, J., & Zhang, W. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1809-5_14 · Cited by 1
Empowering Future Spectrum Management and Regulation With Large Language Models
Rutagemwa, H., Ghasemi, A., & Guinand, P. S. (2025) · IEEE Intelligent Systems · doi:10.1109/mis.2025.3567372 · Cited by 1
External Retrievals or Internal Priors? From RAG to Epitome-Augmented Generation by Fuzzy Selection
He, K., Xu, J., Lin, Q., Wang, W., Gao, Z., Wu, J., Huang, Y., & Feng, M. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3581205 · Cited by 1
Flora-RAG: Enhancing Conversational AI with Retrieval Augmented Generation for Floriculture
Shelley, S., Kaur, P., Aggarwal, G., Kaushal, A., & Dutta, M. K. (2025) · International Conference on Educational Technology Management (ICETM) · doi:10.1109/icetm63734.2025.11051979 · Cited by 1
Barbiero, L., Agostini, F., Baci, E., Frigo, F., Vianello, M., Pozza, D., & Campese, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00071-2_32 · Cited by 1
KEFT: Knowledge-Enhanced Fine-Tuning for Large Language Models in Domain-Specific Question Answering
Li, H., Zhang, J., Shen, H., Cheng, K., & Huang, X. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.31 · Cited by 2
Wang, Y., Zhang, Y., Xu, K., Xu, Y., Wang, Y., & Feng, Q. (2025) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai65422.2025.11189813 · Cited by 1
Beyond Similarity: Aligning Retrieval with Generation for Enhanced Image Captioning
Yang, G., Wu, D., An, J., Xiao, S., & Lin, Z. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227734 · Cited by 1
Li, Z., Zhang, H., & Zhang, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228011 · Cited by 1
Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
Tang, M., Ni, S., Guo, J., & Bi, K. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769515 · Cited by 6
TRACE: Topical Reasoning with Adaptive Contextual Experts
Ye, J., Xin, Q., Zhang, C., & Qi, H. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10010031 · Cited by 1
Metadata
- Authors
- Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T.
- Affiliations
- University of Washington (US)
- Stanford University (US)
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 8371-8384
- OpenAlex
- W4401042773
- Cited by
- 209
- References
- 0
- Citations by year
- 2026: 79 · 2025: 96 · 2024: 32
- FWCI
- 59.77
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (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
- retrieval-augmented generation, large language models
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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