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 (NeurIPS 2020), Vol. 33DOI: 10.48550/arXiv.2005.11401 - Introduces Retrieval-Augmented Generation, a method that uses retrieved information to ground language model outputs, enhancing factual accuracy.
Check Your Facts and Try Again: A Simple Way to Improve Factuality of Large Language Models, Akari Asai, Zarik Khan, Yizhong Wang, Ximing Lu, Sewon Min, Arman Cohan, Hannaneh Hajishirzi, 2023Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Association for Computational Linguistics)DOI: 10.18653/v1/2023.acl-long.785 - Presents a method for improving the factual accuracy of LLM outputs through self-correction and iterative fact-checking mechanisms.