Empowering Large Language Models for Textual Data Augmentation

arXiv:2404.17642v1 Announce Type: new
Abstract: With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation. However, the quality of augmented data depends heavily on the augmentation instructions provided, and the effectiveness can fluctuate across different downstream tasks. While manually crafting and selecting instructions can offer some improvement, this approach faces scalability and consistency issues in practice due to the diversity of downstream tasks. In this …

abstract act arxiv augmentation augmented data capabilities cs.ai cs.cl data however language language models large language large language models llms natural natural language quality tasks textual tool type understanding