9 citations · 14 across the 5 of their papers we have counts for
7 papers
Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models
Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia +1
Large language models (LLMs) increasingly serve users whose expectations are shaped by their cultural context, yet most cultural evaluations test what a model knows rather than how…
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…
Do I Really Know? Learning Factual Self-Verification for Hallucination Reduction
Enes Altinisik, Masoomali Fatehkia, Fatih Deniz +4
Factual hallucination remains a central challenge for large language models (LLMs). Existing mitigation approaches primarily rely on either external post-hoc verification or mappin…
FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models
Masoomali Fatehkia, Enes Altinisik, Husrev Taha Sencar
Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural…
PAM: Training Policy-Aligned Moderation Filters at Scale
Masoomali Fatehkia, Enes Altinisik, Mohamed Osman +1
Large language models (LLMs) remain vulnerable to misalignment and jailbreaks, making external safeguards like moderation filters essential, yet existing filters often focus narrow…
Fanar: An Arabic-Centric Multimodal Generative AI Platform
Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39
We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…