Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks

arXiv:2402.13482v1 Announce Type: cross
Abstract: Despite large successes of recent language models on diverse tasks, they suffer from severe performance degeneration in low-resource settings with limited training data available. Many existing works tackle this problem by generating synthetic data from the training data and then training models on them, recently using Large Language Models (LLMs). However, in low-resource settings, the amount of seed data samples to use for data augmentation is very small, which makes generated samples suboptimal and less …

abstract arxiv augmentation augmented data cs.ai cs.cl cs.lg data diverse domain language language models large language low performance retrieval retrieval-augmented synthetic synthetic data tasks them training training data training models type