- Published on
Paraphrasing with Large Language Models
- Authors
- Name
- Martin Andrews
- @mdda123
This paper was accepted to the WNGT workshop at EMNLP-IJCNLP-2019 in Hong Kong.
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.
Poster Version
![EMNLP-IJCNLP-2019 WNGT poster thumbnail](/_next/image?url=%2Fstatic%2Fimages%2Fblog%2F2019-11-04_EMNLP-IJCNLP-2019-WNGT-paraphrasing-with-large-language-models_Poster_699x978.png&w=1920&q=75)
Link to Paper
And the BiBTeX
entry for the arXiv version:
@article{DBLP:journals/corr/abs-1911-09661,
author = {Sam Witteveen and
Martin Andrews},
title = {Paraphrasing with Large Language Models},
journal = {CoRR},
volume = {abs/1911.09661},
year = {2019},
url = {http://arxiv.org/abs/1911.09661},
eprinttype = {arXiv},
eprint = {1911.09661},
timestamp = {Tue, 03 Dec 2019 14:15:54 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1911-09661.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}