Yeachan Jun | Searcharxiv

arXiv2026

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

Public open-weight language models are often fine-tuned on private or domain-specific data before deployment, creating a need to audit whether individual records were used during adaptation. We study this problem for discrete diffusion language models (dLLMs), using the pre-fine-tuning checkpoint as a reference. Unlike autoregressive models, dLLMs allow arbitrary mask sets and return predictions for all masked positions in parallel. SAMA averages reconstruction signals over many random masks, which can dilute informative positions and requires repeated model evaluations. We propose JUMP (Joint Uncertainty-Guided Mask Probing), which selects low-reference-confidence positions, masks them jointly, and aggregates clipped target-reference reconstruction gaps from one scoring query per model. Across six MIMIR domains, JUMP raises mean ROC-AUC from 0.819 to 0.902 on LLaDA-8B-Base and from 0.851 to 0.942 on Dream-v0-Base-7B, while using three model forwards per sample versus 32 for SAMA.

cs.AI↗

arXiv2026

Position: The Term "Machine Unlearning" Is Overused in LLMs

Large language models increasingly face demands to "forget" training data, knowledge, or behaviors due to regulatory deletion obligations, copyright/licensing disputes, and safety or product-policy requirements. This position paper argues that machine unlearning is overused as a term in LLM research and should be reserved for dataset-defined deletion: removing the training influence of a precisely specified forget set such that the resulting model is approximately indistinguishable from retraining without that data. We contend that many tasks currently labeled "unlearning" (e.g., refusal for harmful requests, entity/knowledge removal, or targeted suppression) pursue different, often policy-dependent objectives and therefore require different terminology and baselines (e.g., alignment, suppression, editing, obfuscation). We further argue that this confusion is not cosmetic: because papers make different implicit guarantees under the same label, metrics and benchmarks are frequently reused outside their intended scope, rewarding surface-level non-disclosure (e.g., low ROUGE/forget accuracy) even when retraining-equivalence is not tested and derived capabilities remain. We conclude by calling for stricter terminology tied to explicit guarantees and reference models, and for evaluations that match the claimed objective.

cs.CL↗