Papers by Ummar Abbas
Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA (2026.acl-industry)
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Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh, Omar Sinan, Gagan Bhatia, Hamdy Mubarak, Majd Hawasly, Mohammed Qusay Hashim, Kareem Mohamed Darwish, Firoj Alam
| Challenge: | Large language models (LLMs) can answer religious knowledge queries fluently, but they often hallucinate and misattribute sources. |
| Approach: | They propose a bilingual Arabic-English Islamic QA system that uses a multi-agent, tool-augmented architecture to route Islamic queries to specialized modules. |
| Outcome: | The proposed system is based on a multi-agent, tool-augmented architecture and has received over 1.9M accesses in less than a year. |
NxPlain: A Web-based Tool for Discovery of Latent Concepts (2023.eacl-demo)
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| Challenge: | Interpretability of deep neural networks has gained a lot of attention in recent years, especially in NLP, where state-of-the-art models are being widely deployed and used in practice. |
| Approach: | They propose to analyze what linguistic and non-linguistic knowledge is learned within deep neural networks and highlight the salient parts of the input. |
| Outcome: | The proposed tool is useful for debugging, unraveling model bias, and for highlighting spurious correlations in a model. |