Does Synthetic Data Make Large Language Models More Efficient?
Sia Gholami Marwan Omar
Natural Language Processing (NLP) has undergone transformative changes with
the advent of deep learning methodologies. One challenge persistently
confronting researchers is the scarcity of high-quality, annotated datasets
that drive these models. This paper explores the nuances of synthetic data
generation in NLP, with a focal point on template-based question generation. By
assessing its advantages, including data augmentation potential and the
introduction of structured variety, we juxtapose these benefits against
inherent limitations, such as the risk of overfitting and the constraints posed
by pre-defined templates. Drawing from empirical evaluations, we demonstrate
the impact of template-based synthetic data on the performance of modern
transformer models. We conclude by emphasizing the delicate balance required
between synthetic and real-world data, and the future trajectories of
integrating synthetic data in model training pipelines. The findings aim to
guide NLP practitioners in harnessing synthetic data's potential, ensuring
optimal model performance in diverse applications.