Navigating the Synthetic Data Landscape: How to Enhance Model Training and Data Quality

In an increasingly data-driven world, the importance of high-quality, accessible data can’t be overstated. But what do you do when the data you need is hard to come by, expensive, or fraught with privacy concerns?

Enter synthetic data, a game-changing solution that many organizations are turning to. Synthetic data is not a new concept; it existed before the advent of GPT or AI-rush. Many data teams use synthetic data to mix and match with their real-world data for their continuous model training. If you are new to synthetic data, check out our guide on using Stable Diffusion to generate better synthetic datasets.

This blog will explore synthetic data, its applications, and its challenges. We’ll also delve into the role of multi-modal (containing multiple data types e.g. text and numerical) synthetic data in training large language models.

What is Synthetic Data?

Synthetic data is artificially generated data that mimics real-world data’s structure and statistical properties. The main advantage of Synthetic data is its usage as a Privacy Enhancing technique (PET).

PETs are collection of software and hardware tools/approaches that all the data processing while protecting the confidentiality, integrity and availability. These tools strikes a balance between privacy of data subjects and interests of data controllers. Synthetic Data is one such approach used in PET taxonomy of approaches.

Synthetic Data generation tools uses advanced algorithms and generative models, such as Generative Adversarial Networks (GANs), to substitute authentic real-world datasets. For instance, a self-driving car dataset can include synthetic images of roads, GPS coordinates, traffic sounds, and other sensor data, resulting in a multi-modal synthetic dataset.

Why Use Synthetic Data?

Imagine a doctor’s office that wants to make getting vaccinations a better experience for their patients. In this situation, there’s a high chance that private information could be accidentally shared, which is an ethical and legal violation. This makes it hard to study and improve the vaccination process because of the risk of revealing Personal Identifiable Information.

Synthetic data can be designed not to contain any personally identifiable information, making it easier to comply with data protection regulations from various authorities post which you can run the analytics to improve the vaccination process without the risk of disclosure.

Apart from privacy compliance, there are a few other benefits to using Synthetic Data.

Cost-Effectiveness

When it comes to data collection and annotation, relying on real-world data can be a challenging and resource-intensive task. This is particularly true when dealing with sensitive or confidential information or complex data sets with many variables. On the other hand, synthetic data offers a viable and cost-effective alternative to help researchers and data analysts overcome these challenges. Using advanced algorithms and modeling tools, synthetic data can be generated in a controlled and regulated environment, allowing for more efficient and accurate data analysis without the risks or costs associated with collecting and annotating real-world data.

Data Augmentation

Synthetic data can enhance existing datasets, improving the performance and reliability of machine learning models. By introducing synthetic data into existing datasets, machine learning models can be trained on a more extensive and diverse data set, enhancing performance and improving reliability.

Improved Model Accuracy and Efficiency

As discussed earlier, when working with machine learning, one common problem is not having enough data. This shortage of data can make it hard for machine learning models to learn effectively, which can lead to inaccurate predictions or results. However, by using synthetic data, you can simulate a wide variety of situations that might not be represented in the real-world data. This can help make the models more accurate and efficient because they have more and diverse examples to learn from, similar to how a student might learn better with more practice problems.

More AI Interpretability

Modeling the behavior of machine learning systems can be tricky. Synthetic data can play a significant role where data teams can gain deeper insights into how they work, testing the robustness and simulating edge cases or other scenarios. This enables data teams to find weaknesses or vulnerabilities in the model, leading them to build trustworthy AI experiences.

What has changed in a post-GPT world?

With the advent of Generative AI and diffusion models, generating larger multi-modal synthetic datasets has become a common trend. Traditionally, large companies like Google, Amazon or Meta were able to build and train systems at multi-modal scale.

In addition to generating text and image synthetic data, you can create large amounts of video and audio data. This can be useful for small startup companies without huge budgets that want to create an AI experience for thousands of paying users.

Also, multi-modal synthetic data is particularly useful in training large language models like OpenAI’s GPT-4. Such models require vast amounts of diverse data to be effective. This data generated from various sources and in multiple formats, can provide the rich training ground ML models need without the complications of using real-world data.

Challenges with Synthetic Data

According to a Gartner peer insight study from 150 data industry leaders, a fully synthetic dataset is less likely to be used in training due to several reasons listed below.

Data Quality:

About 50% of the respondents to Gartner’s above research say their organization generates synthetic data through a custom-built solution with open-source tools, while 31% turn to vendor-driven solutions. Poorly generated synthetic data aided by low-quality source data can lead to suboptimal model performance. It can cause the model accuracy benefits to be trumped, resulting in bad AI-enabled experiences and solutions.

Synthetic data generated may not always be capable of replicating the complexities and nuances of real-world data. This can result in synthetic data that is too simplistic, does not account for real-world variations, or is not representative of the actual data distribution.

In other words, synthetic data may not capture the full range of scenarios and lack diversity. The generated data may not be realistic enough to be helpful in training machine learning models. This can lead to poor model performance, incorrect predictions, and unreliable AI systems. To overcome this challenge, data scientists need to carefully design synthetic data generation pipelines that account for the complexities and variations in real-world data to ensure that the generated data is realistic and representative of the actual data distribution.

Ethical Concerns

Creating data that is both representative of real-world scenarios and free from any bias is a significant challenge. Ensuring that the generated data accurately portrays the actual data is essential. At the same time, any bias in the data can result in inaccurate models, leading to flawed decision-making.

Time-Consuming

Generating large quantities of data free from any inherent biases can be time-consuming. The process involves collecting, organizing, and analyzing significant information to ensure that it is free from any inherent biases. Creating synthetic datasets that are free of bias and representative of all scenarios is necessary but time-consuming to validate, adding an extra step for data teams.

Fighting the challenges of Synthetic Data

To sum it up, using Synthetic data is inevitable as we progress into AI-verse-enabled solutions. Here is how you could implement a few best practices:

Evaluating Synthetic Data Quality

Data quality is a complex concept encompassing several factors: accuracy, consistency, completeness, and reliability. When evaluating synthetic data produced by datagen tools, it is essential to consider various criteria such as class distribution, inconsistencies, and similarity to real data. However, continuously monitoring, assessing, and enhancing the quality of synthetic data can be challenging. That’s where open-source packages like Cleanlab can come in handy by providing data profiling and performing class distribution analysis.

Validate and Review Synthetic Datasets regularly

Synthetic datasets, by nature, are artificial constructs that approximate the characteristics of real-world data. As such, they must be subjected to continuous scrutiny to ensure they have not drifted from their intended representativeness. One should utilize dataset monitoring using visualization tools like Grafana, that can be used to monitor distribution of features, feature drift analysis.

Implement Model Audit Processes

Model audits are a crucial aspect of working with synthetic datasets. Regular model audits can uncover biases in the synthetic dataset used to train the model. You should measure accuracy, bias, and error rates. Several audit tools allow you to assess more fine-grained aspects of model performance.

Use multiple data sources

Using multiple data sources will increase the diversity of the generated synthetic dataset, making it representative of real-world data. The same diversity will also fill gaps in the dataset, adding newer dimensions.

Assessing the Quality of Synthetic Data with Cleanlab Studio

Synthetic data offers a promising avenue for overcoming many data-related challenges, but it is critical to approach it by focusing on data quality. In the era of Data-centric AI, quality trumps quantity.

At Cleanlab, we are dedicated to enhancing the importance of data quality in the field of AI. Our aim is to simplify the process of creating AI experiences with your data and ensure that it is of the highest quality. You can check out our guide on scoring data quality for synthetic datasets or sign up for Cleanlab Studio to try it for free on your own dataset.