NodeSynth generates evidence-anchored synthetic queries that trigger up to five times higher failure rates in mainstream LLMs than human-authored benchmarks.
Efficacy of synthetic data as a benchmark
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Tabular diffusion models leak membership information via attacks even with partial attacker knowledge, and common heuristic privacy metrics like distance-to-closest-record are unreliable.
Resampling methods achieve near-perfect utility (TSTR 0.997) but fail privacy (DCR ~0), while VAEs balance 83.3% utility with full privacy protection for synthetic educational data.
citing papers explorer
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NodeSynth: Socially Aligned Synthetic Data for AI Evaluation
NodeSynth generates evidence-anchored synthetic queries that trigger up to five times higher failure rates in mainstream LLMs than human-authored benchmarks.
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On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics
Tabular diffusion models leak membership information via attacks even with partial attacker knowledge, and common heuristic privacy metrics like distance-to-closest-record are unreliable.
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Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
Resampling methods achieve near-perfect utility (TSTR 0.997) but fail privacy (DCR ~0), while VAEs balance 83.3% utility with full privacy protection for synthetic educational data.