Synthetic Data Evaluation
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Papers
Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen
Evaluating the quality of synthetic data remains a persistent challenge in the Android malware domain due to instability and the lack of standardization among existing metrics. This work integrates into MalDataGen a Supe…
Synthetic Data EvaluationRoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets
LLMs are powerful generators of synthetic data, which are used for training smaller, specific models. This is especially valuable for low-resource languages, where human-labelled data is scarce but LLMs can still produce…
Synthetic Data EvaluationSynth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
Tabular Generative Models are often argued to preserve privacy by creating synthetic datasets that resemble training data. However, auditing their empirical privacy remains challenging, as commonly used similarity metric…
Synthetic Data EvaluationStruct-Bench: A Benchmark for Differentially Private Structured Text Generation
Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics. While much research literature has foc…
Synthetic Data GenerationSynthetic Data EvaluationText GenerationAn ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics …
scientific discoverySynthetic Data EvaluationWhat's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from statistical distances to predictive perform…
counterfactualFeature ImportanceSynthetic Data Evaluation