LLM in the Loop: Creating the ParaDeHate Dataset for Hate Speech Detoxification

AbstractDetoxification, the task of rewriting harmful language into non-toxic text, has become increasingly important amid the growing prevalence of toxic content online. However, high-quality parallel datasets for detoxification, especially for hate speech, remain scarce due to the cost and sensitivity of human annotation. In this paper, we propose a novel LLM-in-the-loop pipeline leveraging GPT-4o-mini for automated detoxification. We first replicate the ParaDetox pipeline by replacing human annotators with LLM and show that LLM performs comparably to the human annotation. Building on this, we construct ParaDeHate, a large-scale parallel dataset specifically for hate speech detoxification. We release ParaDeHate as a benchmark of over 8,000 hate/non-hate text pairs and evaluate a wide range of baseline methods. Experimental results show that models such as BART fine-tuned on ParaDeHate achieve better performance in style accuracy, content preservation, and fluency, demonstrating the effectiveness of LLM-generated detoxification text as a scalable alternative to human annotation.