关键词: 仇恨言论检测, 大语言模型, 前缀调优, 提示学习

Abstract: With the development of online social platforms, the harm of cyberbullying has become increasingly prominent, with hate speech being one of its manifestations. Researching detection methods for hate speech is conducive to building a healthy internet environment. However, on one hand, previous hate speech detection methods have relied heavily on manual supervision and keyword filtering, which are not only costly in terms of human resources but also lack precision in classification; on the other hand, under the regulation of internet platforms, the expression of hate speech has become more subtle and covert, posing higher demands on the text comprehension capabilities of detection models. Large models like ChatGPT have opened up new possibilities for hate speech detection tasks. Inspired by their exceptional performance in various downstream tasks, a hate speech detection method named P-Prompt, which integrates prefix tuning and prompt learning, has been proposed. Specifically, it first uses the prefix tuning method to fine-tune the large model on relevant datasets, while also incorporating the prompt learning method to enable the model to recognize and focus on hate speech vocabulary in the text to be detected, thereby further enhancing the model's ability to identify hate speech. Experimental results validate the effectiveness of large models in hate speech detection tasks and show that, compared to traditional methods, the P-Prompt method has achieved significant improvements in both binary and multi-class classification tasks of hate speech detection across various evaluation metrics.

Key words: Hate Speech Detection, Large Language Models, Prefix Tuning, Prompt Learning