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This work introduces MISHAP-Bench, a comprehensive benchmark with 12,000 challenging open-ended question-audio pairs and a rigorous evaluation pipeline covering two hallucination categories, and proposes a groundedness judge that uses reference rubrics and judge prompts guided by human annotations.
Guard models are the last line of defense between a language model and a harmful output, yet their training objective is surprisingly narrow. Existing guards learn to predict a single verdict token from a conversational context, concentrating supervision on a single target. The consequences are structural: models latch...
SN-Guided Diffusion is introduced, a fully offline black-box jailbreak framework that steers the diffusion process away from safety-triggering regions using a weighted safety neuron loss, which achieves near-perfect prompt separability.
The two key modules of DecentDP are a parameter selector that designates a subset of parameters for local-only updates, allowing them to be updated without DP noise, and a noise injector that adaptively adds less DP noise to exchangeable parameters that are more sensitive to DP noise.
It is found that two deep graph generative models produce synthetic networks that closely resemble the structural properties of real-world networks, enabling them to identify effective immunization strategies.
SyntheGrAnon is introduced, a framework for evaluating synthetic graph anonymity that primarily targets the singling out, linkability, and inference risks outlined in the EU GDPR at the node and community levels, while also including edge-level attacks as an extension of the node-level setting.
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