Synthetic data: training AI on data generated by other models
Synthetic data is artificially generated information that mimics real-world data's statistical properties, used to train AI models when real data is scarce, sensitive, or costly. It can be fully synthetic, partially synthetic, or hybrid, and is produced via statistical methods, GANs, transformers, or simulations. This approach helps address data privacy, bias, and availability challenges in AI development.
What is synthetic data?
Synthetic data is produced by algorithms or generative AI rather than collected from real-world observations. A synthetic dataset replicates the mathematical properties of the original data, but only for information that is allowed to be inferred, especially when based on the source data's labeling, as explained in AWS's synthetic data overview.
According to IBM's synthetic data article, synthetic data is artificial data designed to mimic real-world data, generated through statistical methods or AI techniques like deep learning and generative AI. Despite being artificially generated, it retains the underlying statistical properties of the original data, allowing it to supplement or even replace real datasets.
Synthetic data can take many forms, including text, numbers, tables, images, and videos. It is often used in fields like healthcare, finance, and robotics where real data is limited, sensitive, or difficult to collect, as noted in NVIDIA's synthetic data generation glossary.
Types of synthetic data
Synthetic data is commonly categorized by its level of synthesis. Fully synthetic data contains no real-world information; it estimates the attributes, patterns, and relationships of real data to emulate it as closely as possible. Partially synthetic data derives from real data but replaces sensitive portions with artificial values, protecting privacy while maintaining characteristics. Hybrid synthetic data combines real datasets with fully synthetic ones, pairing records randomly, as described in IBM's synthetic data article.
AWS's overview also distinguishes between partial and full synthetic data. Partial synthetic data replaces a small portion of a real dataset with synthetic information, useful for protecting sensitive attributes like names and contact details. Full synthetic data is completely generated new, using the same relationships and statistical properties as real data, but containing no actual real-world observations, as stated in AWS's synthetic data overview.
How is synthetic data generated?
Synthetic data generation involves computational methods and simulations to create data that mimics real-world statistical properties. Common approaches include statistical distribution methods, where real data is analyzed to identify underlying distributions and new samples are drawn; model-based methods, where a machine learning model learns to replicate data characteristics; and deep learning methods like GANs and VAEs, which are used for complex data types, as detailed in AWS's synthetic data overview.
Generative AI models, such as transformers and diffusion models, play a significant role in accelerating synthetic data generation. Transformers learn complex patterns in datasets and generate new data that corresponds to the training data, while diffusion models can create high-quality visual content from text or image descriptions, as explained in NVIDIA's synthetic data generation glossary.
The Self-Instruct framework, introduced by researchers at the University of Washington, demonstrates a method for generating synthetic instruction data from a language model itself. The pipeline generates instructions, inputs, and outputs from a language model, filters invalid or similar ones, and then uses them to finetune the original model, as described in the Self-Instruct paper on arXiv.
Applications in training AI models
Synthetic data is used to train AI models in various domains, including robotics, autonomous vehicles, and industrial inspection. In robotics, synthetic data is generated from 3D simulations to train perception models for object detection and obstacle avoidance. For autonomous vehicles, synthetic data augments sensor data to cover rare traffic scenarios, as noted in NVIDIA's synthetic data generation glossary.
The Alpaca model, developed at Stanford, was fine-tuned on 52K instruction-following demonstrations generated using the self-instruct method with OpenAI's text-davinci-003. This approach allowed the team to create a small, replicable model that behaves similarly to larger proprietary models, as reported in Stanford CRFM's Alpaca blog post.
Similarly, the phi-1 model for code generation was trained on a selection of 'textbook quality' data from the web and synthetically generated textbooks and exercises using GPT-3.5. This synthetic data helped achieve high accuracy on coding benchmarks despite the model's small size, as described in the Textbooks Are All You Need paper on arXiv.
Benefits and challenges
Synthetic data offers several benefits, including unlimited data generation, privacy protection, and bias reduction. It can be produced on demand at scale, pre-labeled for machine learning, and used to create datasets that show the same statistical information without exposing sensitive personal data, as explained in AWS's synthetic data overview.
Synthetic data also helps address data scarcity, privacy, and quality issues. It can augment imbalanced datasets to reduce bias and improve model accuracy, and it enables testing in safe environments before real-world deployment, as noted in NVIDIA's synthetic data generation glossary.
However, generating high-quality synthetic data can be challenging. It requires careful modeling to ensure statistical fidelity and avoid introducing biases. Additionally, the use of synthetic data raises regulatory concerns, as highlighted in AWS's synthetic data overview.
| Type | Description | Use case |
|---|---|---|
| Fully synthetic | Contains no real-world data; mimics statistical properties | Testing models when real data is scarce |
| Partially synthetic | Real data with sensitive parts replaced | Protecting privacy in clinical research |
| Hybrid | Combines real and synthetic records | Analyzing customer data without tracing individuals |
Frequently asked questions
What is synthetic data?
Synthetic data is artificially generated data that mimics the statistical properties of real-world data, created using algorithms, simulations, or generative AI models.
Why use synthetic data for AI training?
Synthetic data helps overcome data scarcity, protect privacy, reduce bias, and enable testing in safe environments, especially when real data is limited or sensitive.
What are the types of synthetic data?
The main types are fully synthetic (no real data), partially synthetic (real data with sensitive parts replaced), and hybrid (combines real and synthetic records).
How is synthetic data generated?
It can be generated using statistical methods, model-based approaches, or deep learning techniques like GANs, VAEs, and transformers, as well as simulations.
What are some examples of synthetic data in practice?
Examples include the Alpaca model trained on synthetic instructions, phi-1 for code generation using synthetic textbooks, and synthetic data for robotics and autonomous vehicles.