Can we sustain the AI revolution without running out of data? With over 90 AI applications developed every day in 2023 and demand only soaring in 2024, the pressure on training data is immense. Experts predict there is a high chance that we will run out of quality training data by 2026 if alternatives aren’t found [Source]. This is where synthetic data steps in, promising scalable, diverse training datasets created by generative AI. But, are these synthetic datasets as good and reliable as human-labeled training data? Let’s find the answer to this question through a detailed comparison of Synthetic vs real data in NLP model training.
Pros and Cons of Synthetic Data for Language Models
Just like real-world data, synthetic data has its advantages and disadvantages. Let’s weigh both pros and cons to understand if they are beneficial for your business or not:
Advantages of synthetic data in NLP model training:
- Low cost of acquisition
The most significant benefit of synthetic data in NLP model training is cost efficiency. Real data requires labor-intensive collection processes, manual annotation, and validation by domain experts. Synthetic data eliminates these steps by using automated generative tools; hence, the acquisition cost is low.
Additionally, synthetic datasets allow businesses to experiment with multiple models without worrying about data acquisition costs escalating, fostering innovation even for smaller companies or startups.
- Enhanced data privacy
With real data, one concern is ensuring data privacy. Even after using techniques like data anonymization, sustaining privacy while preserving the usefulness of the real dataset becomes challenging. This issue is solved with synthetic data. Since it doesn’t originate from actual individuals or events, synthetic data inherently avoids issues like identity leakage, GDPR violations, or HIPAA non-compliance.
In sectors like healthcare, synthetic data can replicate realistic patient interactions or medical records without using actual patient data, ensuring compliance while maintaining utility.
- Complete control over customization
Since synthetic data gets created artificially using advanced generative AI and language learning models, developers have full control over its attributes, ensuring that datasets meet their precise requirements. They can generate data that includes rare linguistic structures, dialects, or edge-case scenarios that might be hard to find in real-world datasets.
For instance, a developer training a language model for customer support can create datasets containing:
- Rare linguistic structures such as “Had he but known the truth, he would not have gone” ensure the model understands less frequently used phrasing.
- Dialect-specific variations, such as American English (“elevator”) versus British English (“lift”), or even regional colloquialisms like “y’all” in the Southern United States.
- Edge-case scenarios such as interactions using technical jargon (“The cache timeout needs to synchronize with the backend latency”).
- Improved scalability
Rather than depending on physical data collection and human annotation, synthetic data generation focuses on automating the process, enabling organizations to expand datasets from thousands to millions of samples efficiently.
Using synthetic data for NLP model training eliminates bottlenecks like the need for extensive human intervention, as the scaling process hinges on computational resources instead of manual effort. Additionally, synthetic data generation provides flexibility to quickly create diverse datasets for varied scenarios or edge cases, bypassing the overhead costs and time typically associated with gathering and labeling real-world data. As a result, organizations can scale their datasets based purely on available infrastructure, maximizing efficiency while reducing dependence on human-driven processes.
- Rapid iteration and testing
The ability to quickly generate new datasets allows for rapid prototyping and testing of algorithms. This agility accelerates the development cycle, enabling faster deployment of solutions compared to relying solely on human-labeled training data, which may require extensive time for collection and preprocessing.
Disadvantages of synthetic data in NLP model training:
- Susceptible to inaccuracies
Synthetic data quality is only as good as the model generating it, which might inherit flaws from its training. And since all AI models are susceptible to inaccuracies, guaranteeing accuracy in the generated training datasets through these models is challenging as compared to human-labeled data.
For example, in computer vision applications, a model generating synthetic images of industrial equipment might struggle to accurately render complex surface textures or precise measurements. If the original model has even a small degree of inaccuracy in how it generates specific mechanical parts – like slightly distorting the dimensions of bolts or misrepresenting the way metal surfaces reflect light – these errors will be present in all synthetic images it produces.
- Risk of data bias perpetuation
Since synthetic datasets are not created with real-world data, they may lack sufficient demographic diversity consideration. This means there is a chance that artificially generated synthetic data can have unbalanced data distributions in terms of gender, age, and race. This particularly happens when the AI model used to create synthetic data is not reliable. If the model has underlying biases inherited from its own training data, these errors can propagate into the synthetic datasets.
For example, consider an AI model trained to generate customer support scenarios that are biased toward male names when creating professional email conversations. This bias will carry over into the synthetic dataset, skewing its representation and impacting the performance of downstream applications like chatbots or virtual assistants.
- Data quality and credibility concerns
As the risk of inaccuracies and biases is on the higher side in synthetic data, the datasets created using this approach can be questionable in terms of quality and credibility. At the same time, it is difficult to verify the accuracy of the synthetic data because it is artificially generated and often lacks a benchmark for comparison. For example, if a synthetic dataset is used for training a financial fraud detection system, there’s no easy way to cross-check if the fraudulent transactions represent realistic patterns.
- Lack of real-world complexity
Unlike human-labeled training data, which is rooted in real-world scenarios and experiences, synthetic data lacks authentic context. For instance, a synthetic dataset designed for retail sales predictions might include unrealistic purchasing patterns, such as an unusual spike in luxury goods sales during a recession, which could mislead downstream analytics or predictions.
- High initial setup costs
Although the acquisition cost is low with synthetic data as compared to real-world data, the initial setup cost is significant. These models, such as GANs (Generative Adversarial Networks) or advanced language models, need substantial computational resources for training and fine-tuning. Also, ensuring synthetic data meets the required quality standards involves integrating validation pipelines and tools, which further increases costs. Without these, the generated data might lack reliability, reducing its usability for training models.
The setup cost can further increase if you require custom-built or heavily modified environments to simulate specific scenarios. For example, generating synthetic data for training robots might involve designing a virtual warehouse simulation, complete with dynamic elements like moving shelves and workers. Building such a simulator involves significant upfront investment.
Scenarios where Synthetic Data Excel over Manually Labeled Datasets
While human-labeled training datasets are more reliable, bias-free, and accurate as compared to synthetic data, we cannot deny the fact that the latter is a more viable option if scalability, cost efficiency, and fast time-to-market are your concerns.
Here are some scenarios where synthetic training data can be more beneficial for your business than manually labeled datasets:
- Handling rare or edge cases
Including thousands of edge cases in training datasets in manual labeling can be challenging as it requires subject matter expertise, dedicated time, and resources. However, this becomes easier with synthetic data. Using artificial intelligence, thousands of rare scenarios can be created and added at scale to training datasets, ensuring that AI models can efficiently respond to these edge cases.
Example: An insurance company can create synthetic datasets simulating rare natural disasters, like a tornado hitting a coastal city, to train models for better risk assessment and claims prediction without significantly investing in subject matter experts.
- Ensuring privacy and compliance
In industries dealing with sensitive information, such as healthcare or finance, privacy regulations like GDPR and HIPAA make it challenging to use real-world data. Synthetic data ensures compliance by generating datasets that mimic the patterns of real data without containing actual user information.
- Augmenting real data in time-constrained projects
When tight deadlines make it impractical to collect and label sufficient real-world data, synthetic datasets can supplement limited real-world data to meet project requirements without compromising timelines.
Example: A game development company training NPC (non-player characters) behavior models can generate synthetic player interaction data instead of waiting months for real gameplay data post-launch.
- Simulating data for unpredictable inputs
Certain applications involve highly unpredictable inputs, where capturing all possibilities manually is unrealistic. Synthetic data ensures coverage of these unpredictable edge cases.
Example: In cybersecurity, synthetic datasets can simulate unknown hacking methods or malware patterns to train intrusion detection systems to handle novel threats.
- Bridging gaps in historical data
For applications requiring long-term analysis, synthetic data can fill gaps in historical datasets, providing a continuous timeline for model training or simulation.
Example: A climate research organization can use synthetic data to simulate missing weather patterns from decades ago to enhance forecasting models.
How to Ensure Data Quality in Synthetic Datasets – Best Practices
To work efficiently in all the above-stated scenarios, synthetic datasets must be of high quality. The biggest benefit of manually labeled datasets over artificially generated training data is the data quality. As subject matter experts are involved in manually labeled datasets, they ensure accuracy, contextual relevance, and completeness in the training data. To make sure that synthetic data can also be considered reliable for usage, it must also be free from inaccuracies, bias, and hallucinations. This is possible through some best practices, which involve:
- Use high-quality generative AI models
The first step to ensure data quality in synthetic datasets is to utilize domain-specific, high-end generative AI models. Employ state-of-the-art generative AI frameworks, such as GANs (Generative Adversarial Networks) or advanced language models like GPT, fine-tuned to your domain.
- Involve subject matter experts for bias identification and correction
Human annotators can be incorporated into the framework to analyze the artificially generated training dataset for potential biases, such as demographic, linguistic, or contextual biases, and correct them using their subject matter expertise.
- Validate against real-world data
To ensure that synthetic data covers real-world scenarios, it is crucial to compare it with real-world data. Using statistical analyses (e.g., distribution checks, correlation metrics), data annotation experts can ensure synthetic data maintains fidelity to real-world datasets where applicable.
- Use domain experts for data validation and accuracy check
A robust QA process and manual checks by subject matter experts are crucial to detecting anomalies, unrealistic data points, or imbalanced distributions in synthetic datasets. Since automated tools can sometimes overlook complex errors, involving domain experts in the quality check process is a must. The two most effective ways for integrating human expertise in ensuring the quality of synthetic datasets are:
- Build dedicated teams of data annotation experts in-house
Hire domain experts and provide them with initial training to conduct quality checks for synthetic datasets. Based on your project specifications and goals, these experts can identify outliers in the training data and fix them to provide you with more reliable and accurate synthetic datasets. While this approach offers complete control, you must keep in mind that it requires significant time and investment in recruitment, training, and infrastructure.
- Outsource data labeling services to a reliable third-party provider
Partner with a data annotation service provider for quality management of synthetic data. These service providers have a dedicated team of domain experts who can check training datasets for errors, inaccuracies, and contextual relevance. Utilizing advanced data validation tools and their subject matter expertise, they can ensure your synthetic training datasets remain accurate, reliable, and bias-free. Since you don’t have to invest in resource hiring and training, outsourcing data labeling services proves to be a more cost-efficient approach with faster implementation.
Key Takeaway
The debate between synthetic vs real data in NLP model training isn’t about which is superior—it’s about how to harness the strengths of both. When high-quality, real-world-like synthetic data is generated with care, it becomes a powerful ally for addressing challenges such as data scarcity, model scalability, and training data development cost.
However, the focus must remain on creating datasets that are diverse, unbiased, and relevant to real-world scenarios. By combining the strengths of synthetic and manually labeled datasets, we can build NLP models that are not only efficient but also reliable and adaptable to an ever-evolving world.