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

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

Introduction

Alex from Gretel has presented some remarkable research and findings over the past few months about using agentic systems to create high-quality data, especially in synthetic data creation. This research addresses a significant gap in AI today: the scarcity of high-quality public data. This article will cover various aspects such as the recent academic papers, how Gretel's approach compares to others, and how to get started with their services for creating high-quality instructional data.

Recent Research and Papers

Several noteworthy papers have recently been released. For instance:

• Microsoft: Released the Agent Instruct paper, which aligns closely with Gretel's methods.
• Nvidia: Introduced an open-source pipeline using large models fine-tuned for synthetic data generation.

Both papers indicate that synthetic data can match or even surpass human-generated data at a fraction of the cost.

Gretel's Approach: Navigator

Gretel has a service called Navigator, which we'll explore in detail. We'll examine the agent-based system, comparisons against other AI technologies like GPT-4, and human-generated data. The goal is to give a comprehensive understanding of how to use Gretel for generating high-quality instructional data for AI.

Experimental Setup

Gretel's experiment took an instruction-tuning dataset created by human experts, like Dolly, which included both prompts and Wikipedia articles for ground truth. This allowed a straightforward comparison between AI-generated and human-generated data, resulting in impressive outcomes.

Streamlit App Setup

A Streamlit app has been released by Gretel to facilitate easy data creation. Let's walk through the steps:

1. Validate Key: Log in and validate your Gretel key.
2. Choose Data Source: Select a dataset from Hugging Face, like Dolly.
3. Remove Human Data: Remove human-generated content from the dataset to give the AI raw context.
4. Set Output Format: Instruct the AI on how to structure the output.
5. Choose Models: Select the appropriate models for synthetic data generation.
6. Set Parameters: Define evolutionary steps and mutations for the AI, including specificity, inclusion of necessary context, and complexity targets.
7. Experiment: Start the synthetic data generation process.

The results typically showcased dependable, high-quality data free of hallucinations.

Results & Validation

To validate the synthetic data, Gretel used LLM as a judge. Using OpenAI's latest model, they compared GPT-4 results against Gretel’s own synthetic data. The findings were compelling:

• Gretel's synthetic data outperformed human data 66% of the time without additional modifications.
• By adding other LL models for iterative suggestions, there was a significant bump in data quality.

Conclusion

Gretel’s Navigator has proven to generate high-quality synthetic data efficiently, which can be crucial for training AI models with less but more accurate data. The Streamlit app aids in iterating, fine-tuning, and scaling the synthetic data generation process effectively.

Keywords

• Synthetic Data Creation
• Agentic Systems
• Gretel Navigator
• Instruction Data
• LLM Fine-Tuning
• High-Quality Data
• Evolutionary Steps
• GPT-4
• Hugging Face
• LLM as Judge

FAQ

Q: Can synthetic data really match human expert-generated data? A: Yes. According to recent papers and experiments with Gretel’s Navigator, synthetic data has shown potential to meet or exceed human-generated data in quality.

Q: What models does Gretel use for synthetic data generation? A: Gretel uses an ensemble of smaller, open LLMs that are fine-tuned for synthetic data generation, making their technology highly efficient.

Q: Is Gretel's Navigator free to try? A: Yes, Gretel offers a free tier for users to experiment with their synthetic data generation technology.

Q: How does Gretel ensure the quality of synthetic data? A: Gretel uses an agent-based system with evolutionary algorithms for creating diverse records and a secondary AI “AAA” process to refine and improve data quality.

Q: What datasets can I use with Gretel? A: You can use any dataset available on Hugging Face or import your custom data.

This structure provides a comprehensive guide based on Alex's script, suitable for readers interested in synthetic data generation for AI training.