How to Generate High-Quality Synthetic Data for Fine-Tuning Large Language Models (LLMs)

Introduction

Large Language Models (LLMs) are indeed powerful tools for understanding and generating human-readable languages. However, there are specific use cases where LLMs may fall short. For instance, if you want an LLM to have a deeper understanding of neurology, fashion, sports, or security, how can you achieve that? The answer lies in fine-tuning the LLM with a dataset tailored to the specific use case.

But how do you fine-tune an LLM when most publicly available datasets have already been used to train it? This is a common challenge when trying to improve an LLM by either adding to its training data or fine-tuning it for better performance in various domains.

The solution lies in generating high-quality synthetic data.

In this blog post, you will learn how to generate synthetic data for your specific use case in just a few minutes. So, sit back and relax for an informative read!

The Problem

While working on a personal project, I aimed to fine-tune a Large Language Model (LLM) for a question-and-answer task focused on serving as a cybersecurity help desk. However, I encountered a significant challenge: obtaining a high-quality cybersecurity domain-specific dataset to fine-tune the LLM.

After researching, I discovered an effective solution—using generative AI to create synthetic datasets tailored to specific needs. This led me to a platform called Gratel, which simplifies the process of generating synthetic data, making fine-tuning LLMs for niche applications much more accessible.

What Exactly is Synthetic Data?

Synthetic data refers to data that is artificially created to resemble real-world data in terms of its structure, characteristics, and patterns.

Synthetic data can be generated using a variety of techniques, such as generative models like GANs (Generative Adversarial Networks), or simulations.

These methods offer a flexible, and cost-effective alternative to collecting real-world data, which can be time-consuming, or limited in availability.
Additionally, synthetic data can be used to address privacy concerns, as it doesn’t rely on sensitive personal information. This makes it a valuable resource for industries like healthcare, finance, and autonomous systems.

Benefits of Synthetic Data

Here are five benefits of synthetic data:

1. Cost-effective: Collecting real-world data is expensive and time-consuming. Synthetic data is a cheaper and faster alternative to generating data on demand.

2. Scalable: You can create as much synthetic data as needed This allows you to scale up datasets easily, which is especially helpful for training large AI models.

3. Privacy-friendly: Since synthetic data doesn’t involve real personal information, it reduces privacy risks and helps comply with data protection regulations.

4. Diverse and balanced: Synthetic data can be generated to include underrepresented or rare scenarios, helping improve the fairness and accuracy of AI models.

5. Accessible for testing: It allows developers to test models in different conditions or scenarios without waiting for rare events to happen in real-world data, making the development process more efficient.

Generating Synthetic Data

Now that you have a basic understanding of synthetic data and its benefits, let’s move on to the main topic of this blog post: how to generate it.
This should be a straightforward process, similar to prompting your favourite AI language model to perform tasks. Just follow these steps.

Step 1: Create an account on gratel

Head on to gratel to create an account.

Step 2: Generate data from a prompt

Once you log in to Gratel, you'll have access to the Gratel dashboard. On the dashboard, you'll find the "Prompt to Data" feature. With this, you can easily input your prompt to generate data. Remember, the better your prompt, the better the quality of your data.

For this tutorial and the project I was originally developing before encountering the challenge of generating data, I will be creating cybersecurity-related data. This data will help me fine-tune a language model to answer questions just like a cybersecurity help desk would.

Step 3: Enter prompt to create data

Selecting Gratel's "Prompt to Data" feature will take you to the navigator. There, you can generate an API and get an API key with a single click, which will allow you to start creating datasets.

In the navigator, enter a prompt to instruct Gratel’s language model to generate a dataset.

Here is the prompt I used.

Generate a dataset of cybersecurity-related questions and their corresponding answers, organized by category and difficulty level.
For each question:
Provide a realistic, common cybersecurity question employees or IT staff might ask.
Give a detailed, accurate answer that reflects best cybersecurity practices.
Assign a category from the list of topics provided.
Indicate the appropriate difficulty level based on how technical the question is.

Here is the result:

By default, this will generate a dataset with 50 rows for you to review. To create a larger dataset, such as 1,000 rows, click on the "Batch Data" button.

And that's it! In just a few minutes, you've created a high-quality dataset tailored to your specific use case.

Final thoughts

Creating powerful generative AI applications requires high-quality training data, which can be challenging to obtain. In this article, we've explored how synthetic data can address this issue and how Gratel can help generate high-quality synthetic data efficiently.

Gratel simplifies the process, making it quick and easy to produce quality data. I highly recommend trying it for your next ML or LLM project.

If you have any questions or suggestions, feel free to reach out to me on LinkedIn or Twitter. Happy developing!

Aditional resources:

Link to the generated data: https://www.kaggle.com/datasets/victorkingoshimua/cybersecurity-help-desk