29 citations · 75 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Asim Ersoy, Enes Altinisik, Husrev Taha Sencar +1
Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resou…
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…
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Enes Altinisik, Hassan Sajjad, Husrev Taha Sencar +2
Adversarial training is widely acknowledged as the most effective defense against adversarial attacks. However, it is also well established that achieving both robustness and gener…