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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...
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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