How to Create Synthetic Data at High Quality for Fine-Tuning LLMs

We're at a bit of an AI crossroads, where publicly available data has largely been consumed by existing LLM training. When looking to fine-tune, or even add to pre-training data to improve task performance, access to diverse, labeled datasets is limited. This challenge is particularly acute for organizations seeking to adapt models for specific domains or tasks. In this blog, we'll show you how Gretel Navigator, a versatile compound AI system, enables users to easily create high-quality synthetic data for AI/ML model training. Utilizing an agent-based approach, including techniques such as co-teaching and evolutionary iteration, Navigator’s synthetic data can outperform its own underlying LLMs and even much larger models such as OpenAI’s GPT-4 as shown below. For a quick intro, Navigator is the same service that we use at Gretel to create open datasets like the popular Text-to-SQL dataset. Can’t wait to jump in? Try our low-code synthetic data generation Streamlit app using Navigator.

We're at a bit of an AI crossroads, where publicly available data has largely been consumed by existing LLM training. When looking to fine-tune, or even add to pre-training data to improve task performance, access to diverse, labeled datasets is limited. This challenge is particularly acute for organizations seeking to adapt models for specific domains or tasks. In this blog, we'll show you how Gretel Navigator, a versatile compound AI system, enables users to easily create high-quality synthetic data for AI/ML model training. Utilizing an agent-based approach, including techniques such as co-