<think> So let's replace this phrase with insult... </think> Lessons
  learned from generation of toxic texts with LLMs
Authors: name: Sergey Pletenev, name: Daniil Moskovskiy, name: Alexander Panchenko

Modern Large Language Models (LLMs) are excellent at generating synthetic
data. However, their performance in sensitive domains such as text
detoxification has not received proper attention from the scientific community.
This paper explores the possibility of using LLM-generated synthetic toxic data
as an alternative to human-generated data for training models for
detoxification. Using Llama 3 and Qwen activation-patched models, we generated
synthetic toxic counterparts for neutral texts from ParaDetox and SST-2
datasets. Our experiments show that models fine-tuned on synthetic data
consistently perform worse than those trained on human data, with a drop in
performance of up to 30% in joint metrics. The root cause is identified as a
critical lexical diversity gap: LLMs generate toxic content using a small,
repetitive vocabulary of insults that fails to capture the nuances and variety
of human toxicity. These findings highlight the limitations of current LLMs in
this domain and emphasize the continued importance of diverse, human-annotated
data for building robust detoxification systems.