JUMP: Efficient Membership Inference on Fine-Tuned Diffusion Language Models
JUMP is proposed, an efficient MIA that exploits the ability of dLLMs to predict masked tokens in parallel that improves mean ROC-AUC over a prior multi-mask attack and is extended to the target-only setting by replacing target-reference scoring with relative token preference.
Yeachan Jun, Albert No
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