Abstract: Toxic speech detection is a challenging problem plaguing online social media.While existing methods can effectively identify common toxic information or toxic information generated through specific perturbation patterns,they face two major challenges:1)Due to the diversity of toxicity types and linguistic expressions,training data cannot cover all samples,leading to a shortage of toxic text data for detection techniques;2)Malicious users in real-world scenarios tend to create new perturbation patterns to deceive text toxicity detectors.How to transfer the model’s detection capabilities for old perturbation patterns to new ones has become an urgent issue to address.To address these issues,this paper proposes a few-shot continuous toxicity detection model based on large language model augmentation.The core idea is to use large language models to augment examples in the training set,then combine continuous learning with toxicity detection techniques to ensure the toxicity detection model can continuously and efficiently detect toxicity in text.Additionally,the model not only achieves more precise understanding of features related to different disturbance patterns but also enhances its adaptability and robustness in the few-shot continuous toxicity detection task.The model is tested on the latest DynEscape dataset,and the results demonstrate that it outperforms existing baseline models,achieving optimal performance.

Key words: Toxicity detection, Continual learning, Few-shot learning, Contrastive learning, Large language models