Detoxify: A framework for abusive text transformation using LLMs

📰 ArXiv cs.AI

Learn how to use LLMs to transform abusive text into non-abusive versions with the Detoxify framework

advanced Published 8 Jul 2026
Action Steps
  1. Build a Detoxify model using LLMs to transform abusive text into non-abusive versions
  2. Train the model on a dataset of labeled abusive and non-abusive text samples
  3. Evaluate the model's performance using metrics such as accuracy and fluency
  4. Fine-tune the model to improve its performance on specific types of abusive text
  5. Deploy the model in a content moderation pipeline to automatically transform abusive text
Who Needs to Know This

NLP engineers and researchers can benefit from this framework to develop more effective text transformation models, while product managers can utilize it to improve content moderation in their products

Key Insight

💡 LLMs can be used to effectively transform abusive text into non-abusive versions while retaining the original meaning

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🚫💻 Detoxify: a framework for transforming abusive text into non-abusive versions using LLMs

Key Takeaways

Learn how to use LLMs to transform abusive text into non-abusive versions with the Detoxify framework

Full Article

Title: Detoxify: A framework for abusive text transformation using LLMs

Abstract:
arXiv:2507.10177v2 Announce Type: replace-cross Abstract: Although Large Language Models (LLMs) have demonstrated significant advancements in natural language processing tasks, their effectiveness in the classification and transformation of abusive text into non-abusive versions remains an area for exploration. In this study, we present Detoxify: a framework that employs LLMs to transform abusive text (tweets and reviews) containing hate speech and profanity into non-abusive text while retaining
Read full paper → ← Back to Reads

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