Dr. LLaMA: Improving Small Language Models in Domain-Specific QA via
  Generative Data Augmentation
Authors: name: Zhen Guo, name: Peiqi Wang, name: Yanwei Wang, name: Shangdi Yu

Large Language Models (LLMs) have made significant strides in natural
language processing but face challenges in terms of computational expense and
inefficiency as they grow in size, especially in domain-specific tasks. Small
Language Models (SLMs), on the other hand, often struggle in these tasks due to
limited capacity and training data. In this paper, we introduce Dr. LLaMA, a
method for improving SLMs through generative data augmentation using LLMs,
focusing on medical question-answering tasks and the PubMedQA dataset. Our
findings indicate that LLMs effectively refine and diversify existing
question-answer pairs, resulting in improved performance of a much smaller
model on domain-specific QA datasets after fine-tuning. This study highlights
the challenges of using LLMs for domain-specific question answering and
suggests potential research directions to address these limitations, ultimately
aiming to create more efficient and capable models for specialized
applications. We have also made our code available for interested researchers