LLM in the Loop: Creating the PARADEHATE Dataset for Hate Speech
  Detoxification
Authors: name: Shuzhou Yuan, name: Ercong Nie, name: Lukas Kouba, name: Ashish Yashwanth Kangen, name: Helmut Schmid, name: Hinrich Schutze, name: Michael Farber

Detoxification, 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 an LLM and show that the
LLM performs comparably to human annotation. Building on this, we construct
PARADEHATE, a large-scale parallel dataset specifically for hatespeech
detoxification. We release PARADEHATE as a benchmark of over 8K 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.