TrustAIRLab/HateBench
[USENIX'25] HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
This project helps researchers evaluate how well existing hate speech detection tools can identify harmful content created by large language models (LLMs). It provides a curated dataset of LLM-generated text, labeled as either 'hate' or 'non-hate', including examples crafted to evade detection. Researchers and trust and safety teams can use this to assess detector performance against sophisticated, AI-driven hate campaigns.
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Use this if you need to rigorously test and compare the effectiveness of different hate speech detectors, especially against content generated by advanced AI models.
Not ideal if you are looking for an off-the-shelf hate speech detection solution for real-time content moderation or if you are not comfortable working with potentially offensive content for research.
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Mar 01, 2025
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