This research introduces CSEval, a comprehensive system for automatically evaluating the quality of counterspeech generated to combat online hate speech.

• Provides multi-dimensional assessment across key quality attributes of counterspeech
• Uses auto-calibrated LLMs to achieve reference-free evaluation that aligns with human judgment
• Offers standardized metrics to advance research in automated counterspeech generation
• Creates more reliable measurement tools for content moderation systems

For security professionals, this framework represents a significant advancement in developing more effective automated tools to counter harmful online content while reducing manual moderation needs.

CSEval: Towards Automated, Multi-Dimensional, and Reference-Free Counterspeech Evaluation using Auto-Calibrated LLMs