SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
📰 ArXiv cs.AI
Learn how to improve toxicity detection with explanation using SMARTER, a data-efficient framework leveraging self-augmenting large language models
Action Steps
- Implement a two-stage framework for explainable content moderation using Large Language Models (LLMs)
- Leverage LLMs' own outputs to generate synthetic explanations for both correct and incorrect labels
- Apply preference optimization to align the model with human preferences
- Use self-augmenting techniques to improve the model's performance on toxicity detection tasks
- Evaluate the framework's performance using metrics such as accuracy and explainability
Who Needs to Know This
Data scientists and AI engineers working on content moderation tasks can benefit from this framework to improve the accuracy and explainability of their models
Key Insight
💡 Self-augmenting large language models can be used to improve the accuracy and explainability of toxicity detection models
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🚀 Improve toxicity detection with explanation using SMARTER, a data-efficient framework leveraging self-augmenting LLMs! 🤖
Key Takeaways
Learn how to improve toxicity detection with explanation using SMARTER, a data-efficient framework leveraging self-augmenting large language models
Full Article
Title: SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
Abstract:
arXiv:2509.15174v3 Announce Type: replace-cross Abstract: WARNING: This paper contains examples of offensive materials. To address the proliferation of toxic content on social media, we introduce SMARTER, we introduce SMARTER, a data-efficient two-stage framework for explainable content moderation using Large Language Models (LLMs). In Stage 1, we leverage LLMs' own outputs to generate synthetic explanations for both correct and incorrect labels, enabling alignment via preference optimization wi
Abstract:
arXiv:2509.15174v3 Announce Type: replace-cross Abstract: WARNING: This paper contains examples of offensive materials. To address the proliferation of toxic content on social media, we introduce SMARTER, we introduce SMARTER, a data-efficient two-stage framework for explainable content moderation using Large Language Models (LLMs). In Stage 1, we leverage LLMs' own outputs to generate synthetic explanations for both correct and incorrect labels, enabling alignment via preference optimization wi
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