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School of Computer Science and Information Engineering, Hefei University of Technology
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Rationale-guided knowledge distillation boosts stance detection accuracy in low-resource languages by effectively transferring reasoning knowledge from powerful models to compact student models.
Erasing unwanted concepts from visual generative models is now possible without sacrificing the quality of non-target outputs, thanks to Uni-AdaVD's innovative approach.
