Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges

arXiv:2403.02990v1 Announce Type: new
Abstract: In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection. This survey explores the transformative impact of Large Language Models (LLMs) on DA, particularly addressing the unique challenges and opportunities they present in the context of natural language processing (NLP) and beyond. From a data perspective and a learning perspective, we examine …

abstract arxiv augmentation challenges collection cs.ai cs.cl data data collection examples impact language language models large language large language models llms machine machine learning performance perspectives pivotal survey training type