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CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising
Pre-trained language models have significantly improved sentence representation learning, yet their embeddings remain sensitive to textual perturbations such as synonym substitution, masking, and word dropout. This work proposes a lightweight Contrastive Denoising Autoencoder (CDAE) that refines pre
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