Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, 2020Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 (Curran Associates, Inc.)DOI: 10.48550/arXiv.2005.11401 - Introduces the Retrieval-Augmented Generation (RAG) framework, detailing how the generator component synthesizes information from retrieved documents and an input query to produce a final response.
Retrieval-Augmented Generation for Large Language Models: A Survey, Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang, 2023arXiv preprint arXiv:2312.10997DOI: 10.48550/arXiv.2312.10997 - Provides an extensive overview of RAG, including detailed discussions on the generation component, prompting techniques, and strategies for handling issues like conflicting information and maintaining response quality.