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University of Sheffield
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MultiHashFormer achieves superior performance over traditional Transformers while maintaining a constant parameter footprint, even with multilingual vocabulary expansion.
Safety classifiers leak surprisingly sensitive information: a boundary-targeted attack recovers 19% of user distress conversations from the training data, far exceeding existing membership inference methods.
