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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Most RAG systems retrieve unstructured text chunks and rely on one-shot prompting, which frequently leads to redundant context, low information density, and brittle multi-hop reasoning.
“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]
The Introduction and §3.1 describe retrieval-augmented models retrieving documents to inform a prediction, supporting the document-retrieval part of the claim. Section 3.2 discusses a simple scheme that prepends retrieved documents to the input, but does not say most RAG systems use one-shot generation. The paper does not attribute redundant context, low information density, or brittle multi-hop reasoning to that approach; §9 instead warns that unnecessary retrieval can introduce distracting, irrelevant documents. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredREPLUG reduces the computational burden of finetuning large models by minimizing the KL divergence between retrieval likelihood and the model's output perplexity.
“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
The Introduction says REPLUG can enhance a frozen black-box LM without finetuning it. Sections 4 and 4.3 say REPLUG LSR minimizes KL divergence between retrieval likelihood and an LM score distribution related to perplexity—not perplexity itself. The paper does not establish a reduction in computational burden or discuss a 405B-parameter model. (LLM, conf=0.88)
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Towards Reliable Large Language Models: A Survey on Hallucination Detection
Pan, Y., Kong, L., Wu, J., Yang, Y., Zuo, H., Xiu, Z., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0014-7_37 · Cited by 6 · see citing works
Hint-Enhanced In-Context Learning Wakes Large Language Models Up For Knowledge-Intensive Tasks
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 6 · see citing works
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 5 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 4 · see citing works
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 · see citing works
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 · see citing works
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 3 · see citing works
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 3 · see citing works
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 · see citing works
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 3 · see citing works
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 5 · see citing works
Park, S.-I., & Lee, J.-Y. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00724 · Cited by 5 · see citing works
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 · see citing works
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 3 · see citing works
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 3 · see citing works
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 2 · see citing works
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 · see citing works
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 3 · see citing works
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 2 · see citing works
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 2 · see citing works
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 · see citing works
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 · see citing works
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 6 · see citing works
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 · see citing works
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 2 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · 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
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Li, Z., Zhang, H., & Zhang, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228011 · Cited by 1 · see citing works
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 3 · see citing works
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 · see citing works
Liu, Z., Huang, P., Xu, Z., Li, X., Liu, S., Peng, C., Xin, H., Yan, Y., Wang, S., Han, X., et al. (2026) · AI Open · doi:10.1016/j.aiopen.2026.02.002 · Cited by 1 · see citing works
Augmenting Small Language Model for Better Medical Question Answering through Source Authentication
Das, S., Chatterji, S., & Mukherjee, I. (2026) · ACM Transactions on Information Systems · doi:10.1145/3797887 · Cited by 1 · see citing works
Zhao, S., Wang, C., Liu, Z., Li, T., & Zhai, Y. (2025) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec66846.2025.00071 · Cited by 1 · see citing works
- 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 · 2023: 2
- 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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