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Using LLMs to Generate Synthetic Data for Fine Tuning
The chapter explores the concept of leveraging LLMs to create tagged data for fine tuning, focusing on knowledge distillation to transfer knowledge from large models to smaller ones. It discusses the potential of synthetic data in tasks like speech recognition and computer vision, highlighting challenges in variation and realism in current outputs.