%0 Conference Proceedings %T Paraphrasing with Large Language Models %A Witteveen, Sam %A Andrews, Martin %Y Birch, Alexandra %Y Finch, Andrew %Y Hayashi, Hiroaki %Y Konstas, Ioannis %Y Luong, Thang %Y Neubig, Graham %Y Oda, Yusuke %Y Sudoh, Katsuhito %S Proceedings of the 3rd Workshop on Neural Generation and Translation %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong %F witteveen-andrews-2019-paraphrasing %X Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstream NLP tasks such as text classification, sentiment analysis and question answering with the aid of fine-tuning. We present a useful technique for using a large language model to perform the task of paraphrasing on a variety of texts and subjects. Our approach is demonstrated to be capable of generating paraphrases not only at a sentence level but also for longer spans of text such as paragraphs without needing to break the text into smaller chunks. %R 10.18653/v1/D19-5623 %U https://aclanthology.org/D19-5623 %U https://doi.org/10.18653/v1/D19-5623 %P 215-220