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 the original intent. We evaluate the performance of four state-of-the-art LLMs, such as Gemini, GPT
The proliferation of LLMs and increasing societal concerns about harmful content necessitate robust solutions for content moderation and ethical AI use.
This development addresses a critical challenge for LLM deployment in public-facing applications, potentially improving user experience and mitigating platform risks.
New frameworks for LLM-based content moderation can lead to more nuanced and effective handling of abusive text, moving beyond simple removal to transformation.
- · AI platform providers
- · Social media companies
- · Content moderation services
- · Ethical AI researchers
- · Platforms with weak content moderation
- · Abusive content generators
More sophisticated and nuanced AI-driven content moderation tools become available.
Public discourse on online platforms could become less toxic, leading to improved user engagement and safety.
The development accelerates ethical guidelines and standards for AI in content generation and moderation, demanding greater transparency and accountability from AI models.
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Read at arXiv cs.AI