Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks
This addresses data scarcity in healthcare diagnostics, but it is incremental as it builds on existing LLM and BERT methods.
The study tackled the problem of limited training data for healthcare ML models by using LLMs to generate 4,200 synthetic observations for Autism Spectrum Disorders, which increased recall by 13% but decreased precision by 16% when augmenting a BERT classifier.
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 model accuracy with synthetic training data. We used a BERT classifier pre-trained on biomedical literature to assess differences in performance between models. A random sample (N=140) from the LLM-generated data was evaluated by a clinician and found to contain 83% correct example-label pairs. Augmenting data increased recall by 13% but decreased precision by 16%, correlating with higher quality and lower accuracy across pairs. Future work will analyze how different synthetic data traits affect ML outcomes.