Synthetic Data: Generative AI for Enhanced Data Quality in the Era of Foundational Models

This session explores the transformative potential of synthetic data in the age of foundational language models (LMs). It delves into the synergy between synthetic data and foundational models, examining the impact of data quality, the role of synthetic data in augmentation, and addressing bias and data privacy concerns. The talk covers different types of synthetic data generation, including dummy, data-driven, and simulated data, and discusses various generative models like VAEs, GANs, and Transformers, highlighting their pros and cons for synthetic data generation. The importance of treating data as a product to enhance AI development is emphasized, along with strategies for generating high-quality synthetic data and mitigating potential issues like data contamination.