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20242026
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cs.CL2026

The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs

Pardis Sadat Zahraei, Gokhan Tur, Dilek Hakkani-Tür +1

What happens inside a language model when alignment training conflicts with a cultural value it encodes? Across 16 MENA countries, 26 models, and 1.53M human survey responses, we s…

cs.CL2026

EiCAP: Beyond Fluency, Probing and Improving Emotional Intelligence in LLMs via Psychologically Grounded Multi-Turn Dialogue

Nizi Nazar, Pardis Sadat Zahraei, Dilek Hakkani-Tür +2

Large Language Models increasingly serve in emotionally sensitive roles, including mental health support, education, and crisis response, yet they lack a principled framework for a…

cs.CL2026

Fanar 2.0: Arabic Generative AI Stack

FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34

We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…

cs.CL2026

The Judge Who Never Admits: Hidden Shortcuts in LLM-based Evaluation

Arash Marioriyad, Omid Ghahroodi, Ehsaneddin Asgari +2

Large language models (LLMs) are increasingly used as automatic judges to evaluate system outputs in tasks such as reasoning, question answering, and creative writing. A faithful j…

cs.CL2025

ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoning

Jasin Cekinmez, Omid Ghahroodi, Saad Fowad Chandle +2

We introduce ADAM (A Diverse Archive of Mankind), a framework for evaluating and improving multimodal large language models (MLLMs) in biographical reasoning. To the best of our kn…

cs.CL2025

Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation

Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani +5

Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these i…