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Computer Science > Computation and Language arXiv:2005.11401 (cs) [Submitted on 22 May 2020 ( v1 ), last revised 12 Apr 2021 (this version, v4)] Title: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Authors: 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 View a PDF of the paper titled Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, by Patrick Lewis and 11 other authors View PDF HTML (experimental) Abstract: Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for thei