@inproceedings{witteveen-andrews-2019-paraphrasing,
title = "Paraphrasing with Large Language Models",
author = "Witteveen, Sam and
Andrews, Martin",
editor = "Birch, Alexandra and
Finch, Andrew and
Hayashi, Hiroaki and
Konstas, Ioannis and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke and
Sudoh, Katsuhito",
booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5623",
doi = "10.18653/v1/D19-5623",
pages = "215--220",
abstract = "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.",
}
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<abstract>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.</abstract>
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%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
Markdown (Informal)
[Paraphrasing with Large Language Models](https://aclanthology.org/D19-5623) (Witteveen & Andrews, NGT 2019)
ACL
- Sam Witteveen and Martin Andrews. 2019. Paraphrasing with Large Language Models. In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 215–220, Hong Kong. Association for Computational Linguistics.