#harm-detection News & Analysis

2 articles tagged with #harm-detection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 47/10

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AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety

Researchers introduce AICompanionBench, the first public benchmark dataset for evaluating AI safety in companion platforms like Replika and Character.AI, containing 2,123 annotated conversations across nine risk categories. Testing 20 state-of-the-art LLMs reveals that while models detect explicit harmful content effectively, they struggle significantly with subtle forms of harm like manipulation and frequently misclassify benign conversations.

AINeutralarXiv – CS AI · May 296/10

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MuPHI: Learning Implicit Multimodal Harm Reasoning via Semantically Grounded Reward Optimization

Researchers introduce MuPHI, a dataset and training framework for detecting implicit multimodal harm in image-text pairs where danger emerges from context-dependent reasoning rather than surface features. The proposed MuPHIRM framework uses reward optimization to improve vision-language models' ability to reason about compositional harm while demonstrating stronger generalization to out-of-distribution scenarios.