4 citations · 7 across the 13 of their papers we have counts for
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cs.CL2026
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
cs.CL2026
JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors
Jiho Jin, Junho Myung, Juhyun Oh +5
As large language models (LLMs) are increasingly deployed to users around the world, they are integrated into everyday tasks across diverse cultural contexts, from drafting persona…
cs.CL2026
Investigating Counterfactual Unfairness in LLMs towards Identities through Humor
Shubin Kim, Yejin Son, Junyeong Park +6
Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with humor, their reactions expose t…