Automated Synthetic Data Generation Pipeline Using Large Language Models for Enhanced Model Robustness and Fairness in Deep Learning Systems

In the domain of data-driven decision-making, the availability of robust and unbiased datasets is crucial for training effective deep learning models. This research investigates the potential of Large Language Models (LLMs) to generate synthetic datasets that not only mimic real-world data distributions but also incorporate enhanced diversity and anomaly features. By leveraging the generative capabilities of LLMs, this study introduces innovative methodologies for producing textual, tabular, and structured data, which are then used to train deep learning models across various domains. We assess the authenticity and utility of this synthetic data compared to traditional datasets, focusing on statistical properties and the enhancement of model performance. Furthermore, the paper explores how synthetic data can improve model robustness against adversarial attacks and promote fairness across different demographic groups. Through iterative refinements and automated data augmentation pipelines, our approach continuously adapts to emerging data characteristics, addressing the dual challenge of data scarcity and inherent biases. This research not only broadens the understanding of LLM-based data synthesis but also showcases its practical implications in domains where data collection is restricted by privacy concerns or logistical challenges.