✨Toxicity Ahead: Forecasting Conversational Derailment on GitHub
📝 Summary:
A novel LLM framework uses a two-step prompting pipeline to predict conversational derailment on GitHub. It generates Summaries of Conversation Dynamics to forecast toxicity, achieving high F1-scores and outperforming baselines for proactive moderation.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15031
• PDF: https://arxiv.org/pdf/2512.15031
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For more data science resources:
✓ https://t.me/DataScienceT
#LLM #ToxicityDetection #ContentModeration #GitHub #MachineLearning
📝 Summary:
A novel LLM framework uses a two-step prompting pipeline to predict conversational derailment on GitHub. It generates Summaries of Conversation Dynamics to forecast toxicity, achieving high F1-scores and outperforming baselines for proactive moderation.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15031
• PDF: https://arxiv.org/pdf/2512.15031
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #ToxicityDetection #ContentModeration #GitHub #MachineLearning
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