Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry
This work identifies a phenomenon termed token-level memorization asymmetry through theoretical analysis of diffusion training dynamics and proposes Q-Skew, a quantile-weighted skewness-based indicator for membership inference on finetuned DLMs.
Sheng-Fang Zhai, Leo Marchyok, Yu-Ling Shi et al.
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