From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis
This addresses the problem of expensive data annotation for developers and researchers in sentiment analysis, offering a cost-effective solution, though it is incremental as it builds on existing generative AI methods.
The paper tackles the high cost of data annotation by using ChatGPT to generate synthetic training data for sentiment analysis, enabling smaller models to achieve competitive or superior performance compared to larger models, with improvements in computational efficiency.
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.