Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock

This article discusses how to fine-tune large language models (LLMs) using synthetic data through Amazon Bedrock, addressing challenges of data scarcity in model customization. The key approach involves using a larger "teacher" model to generate synthetic training data for a smaller "student" model.

• Synthetic data generation uses Amazon Bedrock's InvokeModel API to create new training samples
• The process involves using a larger model (like Claude 3 Sonnet) to generate training data for a smaller model (like Claude Instant)
• Experimental results showed synthetic data can improve model performance when original training data is limited
• Evaluation used both LLM-as-a-judge and human evaluation techniques
• The synthetic data approach is most useful when high-quality original data is scarce

The method provides a promising solution for model customization, especially in scenarios with limited training data, demonstrating the potential of synthetic data generation in machine learning.