How to validate your synthetic data set? Our guide
Looking to assess and verify the synthetic dataset you created? You are not alone. A lot of data scientists face this challenge. Synthetic data sets play indeed a critical role in training and testing Machine Learning models... their true value is based on their quality and reliability.
Synthetic data represents computer-generated information that mimics real data while protecting privacy and security. These artificial datasets require more than 1,000 examples for a complete evaluation. Small datasets, often referred to as ”golden datasets” of 100+ examples are enough for consistent testing during AI development. Finally, the validation process requires careful evaluation numerous factors: statistical properties, pairwise distributions, correlations compared to the original data. It is also useful to add some examples annotated by humans. Recent research shows that this improves the quality and effectiveness of a synthetic dataset.
💡 This guide takes you step by step to properly verify your synthetic datasets. You will discover practical methods for setting clear goals and choosing the best techniques for validating your data. These approaches ensure that your synthetic data produces reliable results for machine learning applications in 2025 and beyond.
Why is the validation of synthetic data key in AI?
Synthetic data validation is important in AI. Skipping this step can lead to catastrophic failures for your AI models and applications. Let's see why this validation is not an option, but an obligation if you're serious about your AI developments.
Protecting privacy and data integrity
The main attraction of synthetic data lies in complying with regulations (i.e.: eliminating personal data in particular) while maintaining statistical relevance. However, synthetic datasets do not automatically guarantee confidentiality. Poor validation can expose sensitive information from the original dataset.
2 key metrics are used to validate privacy:
- Leakage score: measures the proportion of items that are similar to the original, which may expose personal data.
- Proximity score: calculates the distance between original and synthetic data. A short distance = increased risk of identification.
Differential confidentiality adds controlled noise during validation. This masks individual contributions and prevents specific information from being inferred, while maintaining the usefulness of the data better than traditional masking techniques.
Avoid biases and hallucinations
Synthetic data present “intersectional hallucinations”: discrepancies with the original data. These discrepancies ensure that they are not mere copies, but can affect model performance.
- Example: when extracting relationships, reminders can drop from 19.1% to 39.2%.
- Some hallucinations are benign, others seriously harmful.
Validation should verify:
- Statistical similarity with the original data
- The absence of biases or undesirable motives
- The impact of hallucinations on downstream tasks
💡 GAN-based methods can reinforce existing biases. Your validation should check the representativeness of different demographic groups to avoid discriminatory results.
Ensuring real applicability
Synthetic data should work in practical cases. Effective lab models can fail in the field if validation is neglected.
Researchers recommend 2 methods:
- TSTR (Train Synthetic Test Real)
- TRTR (Train Real Test Real)
👉 Scores (0 to 1) measure the ability of synthetic data to maintain the predictive power of real data. Closer to 1 = better.
🔎 Here's an example of a good evaluation framework available on arXiv: A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models, https://arxiv.org/html/2404.14445v1
Validating the importance of variables is just as critical: it ensures that variables maintain their role in predictions. With good validation, the models reach 95% of predictive performance models trained on real data.
Cross-validation builds trust: the opinion and knowledge of experts in the field (finance, legal, medicine, etc.) helps to detect inconsistencies that automatic tools miss.
Step 1: Define the purpose of your data set
Before any validation, you need to know what you want from your synthetic data, before even thinking about using it to train or fine-tune an AI model.
Assessment vs. training vs. simulation
- Workout: useful in case of rare or unbalanced data (e.g. for the detection of fraud).
- Assessment: Many experts emphasize the importance of synthetic data for scenario testing and privacy.
- Simulation: in health, they make it possible to create realistic patient records without exposing sensitive information.
Golden datasets vs. exploratory sets
- Golden datasets: small, reliable and constant games to measure performance.
- Exploratories: more extensive and varied, used during development.
How many examples?
- Assessment : for quite a few use cases, 1,000+ examples give a complete vision. 100+ are enough for consistent tests during development.
- Workout :
- 100 examples = poor quality
- Strong improvement between 100 and 1,600 examples
- Plateau after 6'400 examples
Step 2: Choosing the right validation techniques
Manual review and expertise
Experts detect problems that statistics miss (cultural nuances, ethics, business inconsistencies). Adding a few examples annotated by humans greatly improves the quality.
Cross-benchmarking between models
Ex: generate with GPT-4, check with Mistral Large 2.
Compare TSTR and TRTR. A dataset that maintains 95% of the predictive power is ready for real uses.
Comparison with real data
- Kolmogorov-Smirnov for continuous variables
- Total Variation Distance for categories
- Coverage of beaches and categories
- Similarity of missing values
Step 3: Use metrics to validate
Three key dimensions:
- Fidelity
- KS, Chi-square tests
- Mutual correlations and information
- Visual verification (histograms, matrices)
- Usefulness
- TSTR + TRTR
- Scores close to 1 = high utility
- Importance of variables (up to 0.93 in correlation score)
- Confidentiality
- Exact pairing score (must be zero)
- Attack tests by inference of belonging
- Differential privacy with added noise
💡 You have to find a balance between fidelity, usefulness and confidentiality depending on the use case.
Step 4: Combine human and automated validation
When to mobilize human annotators
- Complex fields (health, finance, legal)
- Sensitive cases (content moderation)
- Ambiguous cases that automation handles poorly
LLMs as judges
LLMs offer an economical alternative for judging the quality of text outputs.
Fast process:
- Define criteria
- Create a small validation dataset
- Manually annotate this dataset
- Write an accurate assessment prompt
- Iterate
Improving few-shot learning
Mixing human and synthetic data significantly improves performance.
- Adding 2.5% human data is most of the time enough to make a real difference (but we do recommend a bit more for your experimentations...).
- The quality only drops sharply if we eliminate the 10% final of human data.
Conclusion
The validation of synthetic data sets remains a mandatory step in the development of artificial intelligence, in particular for LLM finetuning.
- Why?: ensure confidentiality, reduce bias, guarantee real applicability.
- How?: define a clear objective, choose appropriate techniques, measure with reliable metrics, combine humans and automation.
- Result: with a small proportion of human data (between 5 and 10%, sometimes less), we greatly improve quality.
💡 In 2026, synthetic data will become essential, especially in the face of tighter regulations. The companies that will master the validation will have a real competitive advantage!
