3 accepted papers
A fundamental yet overlooked limitation of current deepfake detection benchmark is the lack of evaluation frameworks that align technical accuracy with real-world impact. We argue that technical metrics may fail to capture models' actual capacity to mitigate real-world harm, as they treat all errors
While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training. Memory-efficient Zeroth-order (MeZO) optimizers, recently proposed to address this issue, only require fo…
Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address this by designing classifiers to categorize classes known during training, while grouping unknown or new classes into an "other" category. However, they…