Summary of Parafusion: a Large-scale Llm-driven English Paraphrase Dataset Infused with High-quality Lexical and Syntactic Diversity, by Lasal Jayawardena and Prasan Yapa
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity
by Lasal Jayawardena, Prasan Yapa
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary ParaFusion, a large-scale English paraphrase dataset, addresses existing challenges in natural language processing (NLP). Current datasets lack diversity and contain noise, hate speech, and non-English sentences. ParaFusion utilizes Large Language Models (LLM) to develop high-quality data with enhanced lexical and syntactic diversity, while maintaining semantic similarity. The new dataset offers at least 25% improvement in both measures, as evaluated across several metrics. Additionally, the paper proposes a gold standard for paraphrase evaluation, providing one of the most comprehensive strategies to date. ParaFusion has the potential to be a valuable resource for improving NLP applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about creating a new dataset that helps computers understand language better. The problem is that current datasets are not good because they don’t have enough variety and contain bad things like hate speech. This new dataset, called ParaFusion, uses special computer models to create high-quality data with more words and sentence structures. It’s cleaner and more focused than what we had before. The results show that this new dataset is much better at helping computers understand language. It can even help us evaluate how well computers do at understanding language. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
