From Big to Small Without Losing It All: Text Augmentation with ChatGPT
  for Efficient Sentiment Analysis
Stanisław Woźniak Jan Kocoń
In the era of artificial intelligence, data is gold but costly to annotate.
The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT
for text augmentation in sentiment analysis. We leverage ChatGPT's generative
capabilities to create synthetic training data that significantly improves the
performance of smaller models, making them competitive with, or even
outperforming, their larger counterparts. This innovation enables models to be
both efficient and effective, thereby reducing computational cost, inference
time, and memory usage without compromising on quality. Our work marks a key
advancement in the cost-effective development and deployment of robust
sentiment analysis models.