Year: 2026
Type: conference-paper
Abstract: Arabic humor provides a challenging diagnostic test for large language models because interpreting jokes often requires pragmatic inference, sociolinguistic awareness, and culturally grounded knowledge that standard NLP benchmarks do not evaluate.Arabic is particularly suitable for probing these abilities given its diglossic structure and dialect diversity, where humor frequently arises from register contrast, dialect-specific vocabulary, and shared cultural references.We propose a three-layer taxonomy of Arabic humor mechanisms covering pragmatic, semantic, and sociolinguistic phenomena, illustrated through thirteen curated examples spanning Egyptian, Levantine, Gulf, Tunisian, and Iraqi Arabic.Building on this taxonomy, we introduce a diagnostic evaluation framework using contrastive minimal pairs, a multi-dimensional scoring rubric, and a cultural presupposition ontology.A small proof-of-concept probing study with GPT-4o, Gemini 2.0 Flash, and Claude Sonnet 4.5 reveals recurring failure patterns in sarcasm interpretation, register contrast reasoning, dialectal vocabulary coverage, and cultural grounding.We position this work as a diagnostic framework and pilot, not a mature benchmark, and outline a path toward larger annotated resources.
Author: Wajdi Zaghouani
Institution: Northwestern University in Qatar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166836508