1 citations · 1 across the 3 of their papers we have counts for
7 papers
From RAG to Agentic RAG for Faithful Islamic Question Answering
Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar +8
Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. Yet standard MCQ/MRC-style e…
Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA
Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh +7
Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic sett…
NativQA Framework: Enabling LLMs and VLMs with Native, Local, and Everyday Knowledge
Firoj Alam, Md Arid Hasan, Sahinur Rahman Laskar +3
The rapid progress of large language models (LLMs) raises concerns about cultural bias, fairness, and performance in diverse languages and underrepresented regions. Addressing thes…
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…
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…
Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questions
Farah Atif, Nursultan Askarbekuly, Kareem Darwish +1
Despite the increasing usage of Large Language Models (LLMs) in answering questions in a variety of domains, their reliability and accuracy remain unexamined for a plethora of doma…