Independent sampling improves diffusion-model training-data detection
A new method helps audit whether diffusion language models are exposing memorized training data, even when query budgets are tight. Independent Token Sampling chooses masked tokens with weak estimated dependencies, then combines their signals; on the ArXiv dataset, it improved AUC by 0.18 over existing methods across the paper’s experiments.