A Guide to Responsible Synthetic Data Creation

introduction

In the era of AI and machine learning, data is the key to training and fine-tuning models. However, collecting high-quality, diverse data sets can be difficult. Synthetic data generation offers a promising solution, but how do you ensure that the data you generate is valid and accountable?

In this blog, we’ll look at the process we use to create responsible synthetic data, evaluate it, and fine-tune our models. We also Azure AI The RAFT distillation recipe is a new approach to generate synthetic datasets using Meta’s Llama 3.1 model and UC Berkeley’s Gorilla project.

Understanding synthetic data for fine-tuning

What is synthetic data?

Synthetic data is not collected from real events but is artificially generated. It is used when collecting real data is expensive, time-consuming, or raises privacy concerns. For example, you can generate synthetic images, videos, or text to mimic real-world data sets.

Why synthetic data is important for fine tuning:

Fine-tuning machine learning models with real data is often limited by the availability of diverse, high-quality datasets. Synthetic data fills these gaps by providing additional samples, augmenting the original dataset, or creating new, previously unseen scenarios. For example, in AI models such as GPT or image classification systems, fine-tuning using synthetic data helps the model adapt to special tasks or environments.

Common use cases:

• Natural Language Processing (NLP): Generate new text to help the model better understand unusual language structures.
• Computer Vision: Generate synthetic images to train models for object detection, especially in rare or sensitive cases such as medical imaging.
• Robotics: Reduce the need for real-world testing by simulating the environment your AI model will interact with.

What makes data “responsible”?

Synthetic data can exacerbate existing biases or raise new ethical issues. Responsible data generation ensures that datasets are fair, representative, and do not produce harmful consequences when used to fine-tune AI models.

Key principles of responsible synthetic data include:

• equity: Avoid bias about race, gender, or other sensitive attributes.
• seclusion: Ensures that synthetic data does not leak sensitive information from real datasets.
• transparency: Document the origin and processing of synthetic data.

Quality aspects for verification:

• manifold: Does the data capture the range of possible real-world cases?
• relevance: Does the synthetic data match the domain and task for which it will be used?
• Performance impact: Does using synthetic data improve model performance without compromising fairness or accuracy?

Synthetic data validation

Validation ensures that synthetic data meets the required quality and ethical standards before being used to fine-tune the model.

Verification technology:

• Actual comparison: If you have real data available, compare the synthetic data to the real data set to see how closely it matches.
• Model-based verification: Fine-tune the model using both synthetic and real data, then test its performance on the validation dataset. Synthetic data is considered valid if it significantly improves the model’s accuracy or generalization ability.
• Bias and Fairness Assessment: Use fairness metrics (e.g. demographic parity or differential impact) to determine whether synthetic data introduces unintended bias. Several tools, such as Microsoft Fair Learn or IBM’s AI Fairness 360, can help identify these issues.

Tools and methods for verification:

• Azure Machine Learning It provides built-in tools for data validation, including feature importance, explainability dashboards, and fairness assessments.
• open source tools Tools like Google’s What-If tool or IBM AI Fairness 360 can provide detailed reports on the fairness and bias of synthetic data.

RAFT Distillation

RAFT distillation recipes are available at: GitHubWe present a method for generating high-quality synthetic datasets using Meta Llama 3.1 and UC Berkeley’s Gorilla project.

About RAFT

Reinforcement Active Fine-Tuning (RAFT) is a technique used to fine-tune the same or similar models after a pre-trained model has been generated on synthetic data. The goal is to generate data that is relevant, diverse, and consistent with the task for which the model is being fine-tuned.

Metarama 3.1:

Deployed powerful language model Azure AI. Meta Llama uses RAFT to generate synthetic data that can be used for NLP tasks such as question answering, summarization, or classification.

UC Berkeley’s Gorilla Project:

The Gorilla project focuses on fine-tuning models for specific tasks using minimal data. By integrating the Gorilla project’s methods into RAFT, users can quickly and efficiently create custom datasets.

Steps in the RAFT Distillation Recipe:

• Step 1: Deployment of Meta Llama 3.1 Azure AI Use the instructions provided in the GitHub repository.
• Step 2: Create a synthetic dataset using RAFT distillation. This involves having the model generate relevant text or data samples based on input prompts.
• Step 3: Evaluate the generated synthetic datasets using metrics such as relevance, diversity, and performance.
• Step 4: Use the generated synthetic data to fine-tune your model to improve performance on specific tasks.

The blog may include code snippets from the repository to show users how to set up RAFT in Azure, create synthetic datasets, and fine-tune models.

To create a JSON Lines (JSONL) file for your training model: Azure Machine LearningPlease follow these step-by-step instructions:

What is a JSONL file?

A JSONL file is a format where each line is a valid JSON object. It is commonly used for machine learning tasks such as model fine-tuning because it allows structured data to be stored in a readable format.

Step-by-step guide to creating JSONL files

Step 1: Prepare your data

• Identify the data needed for fine tuning. For example, when fine-tuning a text model, your data might consist of input and output text pairs.
• Each line in the file must be a JSON object. The general structure is as follows:

Step 2: Use a text editor or Python script

You can generate a JSONL file using a text editor such as Notepad or VS Code, or programmatically using a script (e.g. Python).

• Method 1: Use a text editor
• Open a plain text editor, such as Notepad++ or Visual Studio Code.
• Write each line as a valid JSON object. yes:

• Save your file Use the .jsonl extension (e.g. training_data.jsonl).

Method 2: Use a Python script You can also use Python to generate JSONL files, especially if you have a large data set.

Example Python code:

Step 3: Validate JSON Format

Please check the following:

• Each line in the file is A valid JSON object.
• there is no comma Between objects (unlike JSON arrays)
• Make sure all objects are properly contained within {}.

Step 4: Upload to: Azure ML

Once your JSON file is ready:

1. file upload Go to your Azure Machine Learning workspace. You can do this through the Azure portal or SDK commands.
2. Use files For training or evaluation Azure ML Configure pipelines based on tasks (e.g. fine-tuning).

Step 5: Test your files

To check the file, you can use a simple Python script to load and print the contents.

Example: JSONL file for fine tuning (3 lines)

Summary of steps:

1. data preparation Structured JSON format.
2. Write each line as a separate JSON object In a text editor or using a Python script.
3. Save your file Uses the .jsonl extension.
4. Type Verification Verifies that each line is a valid JSON object.
5. Upload to Azure Machine Learning For model training or fine-tuning.

Following these steps will prepare a valid JSONL file that you can use in: Azure Machine Learning Used for tasks such as model fine-tuning.

resources

1. Azure Machine Learning documentation: https://learn.microsoft.com/azure/machine-learning/?view=azureml-api-2&viewFallbackFrom=azureml-api-…
2. Azure AI: https://azure.microsoft.com/solutions/ai/?studenttamb_263805