Synthetic Data Generation Methods for LLMs: A Comprehensive Guide

Author(s): M

Originally published on Towards AI.

A Practical Guide for ML Engineers and Researchers.

If you’ve been following the AI landscape, you’ve probably noticed a paradox. While large language models are getting bigger and more capable, the high-quality data needed to train them is becoming increasingly scarce.

The article discusses the growing challenge of obtaining high-quality data for training large language models (LLMs) and introduces synthetic data as a promising solution. It highlights various methods for generating synthetic data, including self-instruction, distillation from larger models, and rule-based generation. Also noted are the challenges and limitations associated with synthetic data, such as model collapse and bias amplification. The piece concludes with real-world applications of synthetic data across industries, emphasizing its increasing importance in AI development.

Read the full blog for free on Medium.

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Published via Towards AI