📚 A Survey on Data Synthesis and Augmentation for Large Language Models

This research paper examines the use of synthetic and augmented data to enhance the capabilities of Large Language Models (LLMs). The authors argue that the rapid growth of LLMs is outpacing the availability of high-quality data, creating a data exhaustion crisis. To address this challenge, the paper analyzes different data generation methods, including data augmentation and data synthesis, and explores their applications throughout the lifecycle of LLMs, including data preparation, pre-training, fine-tuning, instruction-tuning, and preference alignment. The paper also discusses the challenges associated with these techniques, such as data quality and bias, and proposes future research directions for the field.

📎 Link to paper