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 33, Vol. 33 (NeurIPS) - Introduces the Retrieval-Augmented Generation (RAG) architecture, which forms the basis for integrating external knowledge into LLMs and highlights the need for effective context management.
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 - A comprehensive survey of Retrieval-Augmented Generation, reviewing its evolution, architectures, and various methods for addressing challenges like context length limitations and effective knowledge integration.