Paraphrasing with Large Language Models

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 pre...

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Bibliographic Details
Published inarXiv.org
Main Authors Witteveen, Sam, Andrews, Martin
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.11.2019
Subjects
Computer Science - Computation and Language
Computer Science - Learning
Data mining
Downstream effects
Large language models
Sentiment analysis
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Summary: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.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1911.09661