Retrieval-augmented Generation
Retrieval-Augmented Generation is a machine learning framework that integrates external knowledge retrieval with text generation by conditioning the generation process on retrieved documents to improve the factual accuracy and relevance of generated responses.
197
Publications
15.3K
Citations
1K
Authors
272
Institutions
Publications per year
2017–2026
196
197
Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao, Yun Xiong, Xinyu Gao et al. · arXiv (Cornell University) · 2023 · 658 citations · Full text
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Wenqi Fan, Yujuan Ding, Liangbo Ning et al. · 2024 · 559 citations
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
Matthew Dunn, Levent Sagun, Mike Higgins et al. · arXiv (Cornell University) · 2017 · 385 citations · Full text
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao et al. · 2023 · 333 citations · Full text
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272
Nanyang Technological University
Singapore, Singapore
7 h-index
28 publications
1.7K citations
Harvard University
Cambridge, United States
6 h-index
34 publications
1.3K citations
Stanford University
Stanford, United States
5 h-index
20 publications
1.4K citations
National University of Singapore
Singapore, Singapore
5 h-index
12 publications
1.2K citations
Google (United States)
Mountain View, United States
5 h-index
9 publications
581 citations
Meta (Israel)
Tel Aviv, Israel
5 h-index
19 publications
3.8K citations
Microsoft Research Asia (China)
Beijing, China
5 h-index
10 publications
1K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
19 publications
1.5K citations
1–8 of 272
Repository · 55 publications · 5.8K citations
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5 publications · 495 citations
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Repository · 5 publications · 168 citations
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Conference · 5 publications · 514 citations
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Repository · 4 publications · 73 citations
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