<think> So let's replace this phrase with insult... </think> Lessons
  learned from generation of toxic texts with LLMs
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.
68c287b329b8ec9932cd0a3a
Sergey Pletenev
claimed_verified
2025-09-12T16:12:09.884Z
5dfa8e07da6d0311fd3d5430
https://cdn-avatars.huggingface.co/v1/production/uploads/1651090418656-5dfa8e07da6d0311fd3d5430.png
Sergey Pletenev
user
memyprokotow
68c287b329b8ec9932cd0a3b
Daniil Moskovskiy
claimed_verified
2025-09-12T16:12:06.139Z
61ade264f602880813dbe10b
/avatars/a92dea7d853bbabbf60b351c207b6875.svg
Daniil Moskovskiy
user
etomoscow
68c287b329b8ec9932cd0a3c
Alexander Panchenko
claimed_verified
2025-09-16T09:43:26.646Z
605473729d7c1d4d81b7e52b
https://cdn-avatars.huggingface.co/v1/production/uploads/1662046050710-605473729d7c1d4d81b7e52b.jpeg
Alexander Panchenko
user
apanc
<think> So let's replace this phrase with insult... </think> Lessons
  learned from generation of toxic texts with LLMs
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.
Models fine-tuned on synthetic toxic data generated by LLMs perform worse than those trained on human data due to a lexical diversity gap in the synthetic content.
Large Language Models LLMs text detoxification Llama 3 Qwen ParaDetox SST-2 fine-tuned lexical diversity gap