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Current dense predictors can deviate by over 3x from accurate predictions when faced with unsupported edge cues, revealing a fundamental flaw in their design.
PRISM achieves a breakthrough in empathetic dialogue systems by seamlessly integrating prosody with language, leading to enhanced emotional expression and response quality.
Test-time performance can be significantly improved without a corresponding drop in validation loss, raising critical questions about model evaluation in deep learning.
Seemingly harmless fine-tuning data can stealthily nudge LLMs toward unsafe behavior by subtly shifting model parameters in "danger-aligned" directions.
Adversarial training can be made more effective by considering the hierarchical relationships between classes, leading to vision-language models that are more robust to attacks on both specific classes and their broader categories.
