ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity

arXiv:2404.12010v1 Announce Type: cross
Abstract: Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that closely resemble the source sentences. Moreover, these datasets often contain hate speech and noise, and may unintentionally include non-English language sentences. This research introduces ParaFusion, a large-scale, high-quality English paraphrase dataset developed using Large Language Models (LLM) to address these challenges. ParaFusion augments existing datasets with high-quality data, significantly enhancing …

abstract arxiv cs.ai cs.cl cs.lg dataset datasets diversity domain english hate speech language language processing llm natural natural language natural language processing nlp noise pivotal processing quality resemble scale speech type