Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor
This addresses safety challenges in AI for culturally nuanced humor detection, though it is incremental as it builds on existing toxicity datasets with new categorizations.
The paper tackled the problem of detecting harmful humor by introducing a multimodal, multilingual benchmark with 3,000 texts, 6,000 images, and 1,200 videos, finding that closed-source models significantly outperform open-source ones with notable performance differences between English and Arabic languages.
Dark humor often relies on subtle cultural nuances and implicit cues that require contextual reasoning to interpret, posing safety challenges that current static benchmarks fail to capture. To address this, we introduce a novel multimodal, multilingual benchmark for detecting and understanding harmful and offensive humor. Our manually curated dataset comprises 3,000 texts and 6,000 images in English and Arabic, alongside 1,200 videos that span English, Arabic, and language-independent (universal) contexts. Unlike standard toxicity datasets, we enforce a strict annotation guideline: distinguishing Safe jokes from Harmful ones, with the latter further classified into Explicit (overt) and Implicit (Covert) categories to probe deep reasoning. We systematically evaluate state-of-the-art (SOTA) open and closed-source models across all modalities. Our findings reveal that closed-source models significantly outperform open-source ones, with a notable difference in performance between the English and Arabic languages in both, underscoring the critical need for culturally grounded, reasoning-aware safety alignment. Warning: this paper contains example data that may be offensive, harmful, or biased.