National University of Science and Technology Politehnica Bucharest

 

Description: This tutorial provides a technical overview of synthetic data generation using Large Language Models (LLMs), focusing on core methodologies and their integration. Synthetic data has become an essential tool for addressing key limitations in the availability, cost, and distributional coverage of manually annotated datasets. It enables scalable experimentation, facilitates data augmentation in low-resource settings, and supports iterative model refinement. The session begins with a discussion of generation methods and filtering strategies designed to enforce quality constraints. Next, the tutorial examines practical use cases. These include alignment tuning, where synthetic datasets are used to steer model behavior; inference-time augmentation, where generated exemplars support few-shot generalization or contextual adaptation; and self-improvement workflows, where models contribute to their iterative training through synthetic supervision.