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

🌈 Abstract

The article discusses the challenges of accessing high-quality, diverse datasets for training and fine-tuning large language models (LLMs), and how Gretel Navigator, a compound AI system, can be used to generate synthetic data to address this problem. The article covers the following key points:

🙋 Q&A

[01] The Scarcity of High-Quality Datasets

1. What is the key challenge faced when trying to adapt LLMs for specific domains or tasks? The key challenge is the limited access to diverse, labeled datasets required for fine-tuning or adding to pre-training data to improve task performance.

2. How is synthetic data emerging as a solution to enhance LLM performance? Synthetic data has emerged as a promising technique for enhancing the performance of LLMs, both during pre-training and fine-tuning for specific tasks. Many leading model teams are recognizing synthetic data as a crucial element in developing more advanced and capable models.

[02] Generating Synthetic Question-Answer Pairs

1. What dataset is used as an example for generating synthetic question-answer pairs? The Databricks Dolly 15k dataset, specifically focusing on the closed question-answering task, is used as the example dataset.

2. What are the key parameters in the InstructionResponseConfig class used to configure the synthetic data generation? The key parameters include input_fields, output_instruction_field, output_response_field, num_generations, population_size, mutation_rate, system_prompt, instruction_format_prompt, instruction_mutation_prompt, instruction_quality_prompt, instruction_complexity_target, response_format_prompt, response_mutation_prompt, response_quality_prompt, response_complexity_target, and use_aaa.

3. How do these parameters help fine-tune the evolutionary process of synthetic data generation? The interplay between these parameters allows for a sophisticated balance between exploration (generating diverse candidates) and exploitation (refining high-quality outputs) in the data generation process, enabling the fine-tuning of the synthetic data to meet specific quality and diversity requirements.

[03] Benchmarking Synthetic Data Quality

1. How was the quality of the synthetic data generated by Gretel Navigator assessed? The quality of the synthetic data generated by Gretel Navigator was assessed by conducting a benchmark study comparing it to human expert-generated data and outputs from state-of-the-art LLMs, using LLM-as-a-Judge (specifically, OpenAI's GPT-4) as an impartial judge.

2. What were the key results of the benchmark study? The benchmark study showed that the synthetic data generated by Gretel Navigator outperformed human expert-curated data from the Databricks Dolly-15k dataset, as well as much larger models like OpenAI's GPT-4, by up to 25.6% on the Closed Question and Answer task.

[04] Versatile Applications of Gretel Navigator

1. What types of data can Gretel Navigator generate? Gretel Navigator can generate high-quality synthetic data for a variety of formats, including text and tabular data, and can be used for tasks such as generating synthetic text, instruction-response pairs, and conversational step-by-step data.

2. What are the key benefits of using Gretel Navigator? Gretel Navigator proves that the smart orchestration of smaller, specialized models can outperform even the largest language models in synthetic data generation across a variety of crucial AI tasks, enabling researchers and developers to create the large, diverse, and high-quality datasets needed to train and fine-tune advanced AI models.

[05] Best Practices for Using Gretel Navigator

1. What are the recommended best practices for using Gretel Navigator? The key best practices include clearly defining your objectives, iteratively refining your prompts, and leveraging the provided tools to experiment and scale effectively.

2. How can users try out Gretel Navigator? Gretel provides an interactive Streamlit app that allows users to quickly refine their approach to synthetic data generation before scaling up their efforts.