  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 both lexical and
syntactic diversity while maintaining close semantic similarity. It also
mitigates the presence of hate speech and reduces noise, ensuring a cleaner and
more focused English dataset. Results show that ParaFusion offers at least a
25% improvement in both syntactic and lexical diversity, measured across
several metrics for each data source. The paper also aims to set a gold
standard for paraphrase evaluation as it contains one of the most comprehensive
evaluation strategies to date. The results underscore the potential of
ParaFusion as a valuable resource for improving NLP applications.
