%0 Journal Article %1 RAG2020 %A Lewis, Patrick %A Perez, Ethan %A Piktus, Aleksandra %A Petroni, Fabio %A Karpukhin, Vladimir %A Goyal, Naman %A Küttler, Heinrich %A Lewis, Mike %A Yih, Wen-tau %A Rocktäschel, Tim %A Riedel, Sebastian %A Kiela, Douwe %D 2020 %J arXiv:2005.11401 %K arxiv retrieval-augmented NLP related_to:b3e9c2b92748097978ba7148601e28d4 posted_with_chatgpt %T Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks %U https://arxiv.org/abs/2005.11401

@article{RAG2020, added-at = {2023-09-13T23:35:43.000+0200}, author = {Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and Küttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rocktäschel, Tim and Riedel, Sebastian and Kiela, Douwe}, biburl = {https://www.bibsonomy.org/bibtex/2eaf7b844dafa872f697d394ba71cf01c/tomvoelker}, description = {The paper discusses the capabilities of large pre-trained language models and their limitations in accessing and manipulating knowledge. The authors introduce retrieval-augmented generation (RAG) models that combine pre-trained parametric and non-parametric memory for language generation. The study explores the effectiveness of RAG models in various NLP tasks and compares them with other architectures.}, interhash = {f7118e46b7fb2897339df74aeb056f34}, intrahash = {eaf7b844dafa872f697d394ba71cf01c}, journal = {arXiv:2005.11401}, keywords = {arxiv retrieval-augmented NLP related_to:b3e9c2b92748097978ba7148601e28d4 posted_with_chatgpt}, timestamp = {2023-09-13T23:35:43.000+0200}, title = {Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks}, url = {https://arxiv.org/abs/2005.11401}, year = 2020 }