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)DOI: 10.48550/arXiv.2005.11401 - Introduces a method to augment language models with a non-parametric memory (retriever) for knowledge retrieval, addressing the limitations of relying solely on parametric memory for long-term knowledge.
Generative Agents: Interactive Simulacra of Human Behavior, Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023arXiv preprintDOI: 10.48550/arXiv.2304.03442 - Describes an architecture for generative agents that simulate believable human behavior through an architectural design that stores and synthesizes past experiences into a long-term memory, enabling agents to remember, reflect, and plan.
A Survey of Large Language Models, Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen, 2023arXiv preprintDOI: 10.48550/arXiv.2303.18223 - Provides a comprehensive survey of large language models, including discussions on their architecture, capabilities, and the challenges of integrating external knowledge and memory for agentic applications.