Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks

arXiv:2405.06695v1 Announce Type: new
Abstract: An important issue impacting healthcare is a lack of available experts. Machine learning (ML) models could resolve this by aiding in diagnosing patients. However, creating datasets large enough to train these models is expensive. We evaluated large language models (LLMs) for data creation. Using Autism Spectrum Disorders (ASD), we prompted ChatGPT and GPT-Premium to generate 4,200 synthetic observations to augment existing medical data. Our goal is to label behaviors corresponding to autism criteria and improve …

abstract arxiv bert cs.ai cs.cl data datasets experts generate healthcare however issue language language models large language large language models machine machine learning networks neural networks patients performance synthetic synthetic data train type