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Found 4 papers, 3 papers with code
Weight Poisoning Attacks on Pretrained Models
Recently, NLP has seen a surge in the usage of large pre-trained models.
Weight Poisoning Attacks on Pre-trained Models
We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure.
Towards Robust Toxic Content Classification
We propose a method of generating realistic model-agnostic attacks using a lexicon of toxic tokens, which attempts to mislead toxicity classifiers by diluting the toxicity signal either by obfuscating toxic tokens through character-level perturbations, or by injecting non-toxic distractor tokens.
Measuring Bias in Contextualized Word Representations
Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks.
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