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Paraphrasing with Large Language Models

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arxiv 1911.09661 v1 pith:PRMZ7XY3 submitted 2019-11-21 cs.CL cs.LG

classification cs.CLcs.LG
keywords textlanguagelargemodelsparaphrasingableachieveadept
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fake News Detection After LLM Laundering: Measurement and Explanation

    cs.CL 2025-01 conditional novelty 5.0 of 10

    LLM paraphrasing of fake news degrades detector performance across 17 detectors, with Pegasus evading best and a sentiment shift that BERTScore fails to capture.

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