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Paraphrasing With Large Language Models

Sam Witteveen, Martin Andrews . Proceedings of the 3rd Workshop on Neural Generation and Translation 2019 – 80 citations

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Affective Computing Compositional Generalization Content Enrichment Fine Tuning Interdisciplinary Approaches Model Architecture Multimodal Semantic Representation Question Answering RAG Variational Autoencoders

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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