LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications. While prior work has made progress in measuring and mitigating bias, it largely focuses on final outputs of models, with limited understanding of the mechanisms that produce biased outcomes. R...
Varsha Ramineni, Hossein A. Rahmani, Jerome Ramos et al.· 0 citations
Safety-aligned LLMs can exhibit emergent misalignment (EM): narrow domain adaptation unexpectedly triggers catastrophic safety failures across unrelated domains. Prior static analyses leave training dynamics unmapped, while existing defenses rely on heuristics that degrade utility. We present a dynamic, second-order ge...
Wei-Qiao Que, Rui-Zhe Li, Cheng-Yu Wang et al.· 0 citations
AMUSER is presented, a multimodal framework which simulates user behaviour and automatically generates prioritised UX improvement recommendations from resulting data and suggests an asymmetric role of multimodality: visual access during simulation improves recommendations through richer traces, while providing visual i...
Anurupa Chowdhury, Bin Wu, Hossein A. Rahmani et al.· 0 citations
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