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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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Hayawi, K., & Shahriar, S. (2025) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet63943.2025.11472215
FlowRAG: Continual Learning for Dynamic Retriever in Retrieval-Augmented Generation
Zhang, S., Shi, T., Song, D., Zhang, L., Zhang, S., Liao, X., & Jin, H. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792361 · Cited by 2
Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks
Dai, T., Zhang, C., & Zou, J. (2026) · Jisuanji shenghuojia. · doi:10.54097/8jvwpk07
Karnatak, N., Chatila, M., Hernandez, D. A. P., Yazdanfar, R., DUGAS, M. A., & Vakis, R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791062
CITRAG : Contradiction Identification and Tracing for Retrieval-Augmented Generation
Huang, S., Lu, J., Xu, Y., Zhang, H., Xu, H., Tang, M., Wang, Y., & Gou, G. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462915
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LLM-Driven MCTS for Conditional Story Generation via Logic-Guided Evidence Tree Optimization
Wu, H., Tian, Z., Huang, Z., Lin, N., Song, Y., Wen, Z., Lu, M., Liu, F., & Li, D. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3687020
SHIFT: Smoothing Hallucinations by Information Flow Tuning for Multimodal Large Language Models
Wang, S., Zhang, Y., Zhu, Y., liu, E., Li, J., Liu, Y., & Ji, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00347
Gu, J., You, F., Pang, X., Tang, Y., Shao, S., & Jiang, J. (2026) · International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508671
VulKnow: Enhancing Vulnerability Detection With Structured Knowledge and Large Language Models
Liao, G., Chen, Y., Ke, W., Wu, H., & Dong, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3697600
Venganti, V., & Rayarao, S. R. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541964
Qi, H., Li, J. D., Ge, Y., Wu, H., Cai, Y., Che, S., Sheng, T., Zhao, Q., & Chen, C. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3700611
Private-RAG: A Privacy-Preserving Retrieval-Augmented Generation Method for Large Model Inference
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Lai, T., Liu, F., Li, G., & Hua, L. (2026) · Viruses · doi:10.3390/v18060656
ImageNetion: Retrieval-as-Policy for Creative Gollin Figure Completion via Generative Feedback
Huang, D., Liu, Y., Qu, J., & Wu, Q. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810636
CONCORD: Asynchronous Sparse Aggregation for Device-Cloud Rag Under Document Isolation
Hu, X., Tang, Z., Yao, Z., Wang, T., & Jia, W. (2026) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws72778.2026.00134
Legal-TREND: Uncertainty Trend Modeling with Domain-Aware Retrieval for Legal Question Answering
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Trust the Eye or the Crowd? Unveiling Conformity in Multimodal Large Language Models
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CHORD: Context Homogeneity Analysis for Robust Defense Against RAG Poisoning Attacks
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Analysis of Reasoning-Intensive Retrieval in Japanese
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Towards Adaptive and Retriever-friendly Retrieval-augmented Generation via Reinforcement Learning
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Learning to Retrieve from Agent Trajectories
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RILEE: Reasoning-Intensive Legal Element Extraction via Hierarchical Re-answering
Wang, L., Hu, J., Ying, Y., Liu, Z., Zhou, Q., Guo, D., & Luo, C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2759-4_4
Retrieval Augmented Generation Enhanced RLHF Framework for Factuality Verification Improvement
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CCF: A Context Compression Framework for Efficient Long-Sequence Language Modeling
Sun, B., & Chao, F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31933-3_42
SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization
Kim, S., Jeong, W., Kim, S., Lee, S., & Lee, D. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818146 · Cited by 4
Training Dense Retrievers with Multiple Positive Passages
Wang, B., Tang, M., Zhang, H., Guo, J., & Bi, K. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818053
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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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