Papers by Rao Anwer
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM (2023.findings-emnlp)
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Sahal Mullappilly, Abdelrahman Shaker, Omkar Thawakar, Hisham Cholakkal, Rao Anwer, Salman Khan, Fahad Khan
| Challenge: | Recent large language models like ChatGPT and Bard excel in a wide variety of NLP tasks but are not specifically tailored for climate related domain specific information. |
| Approach: | They propose a lightweight Arabic Mini-ClimateGPT that is built on an open-source LLM and specifically fine-tuned on a conversational-style instruction tuning curated Arabic dataset Clima500-Instruct. |
| Outcome: | The proposed model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation and human expert prefers it over other open-source models. |
BiMediX: Bilingual Medical Mixture of Experts LLM (2024.findings-emnlp)
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Sara Pieri, Sahal Shaji Mullappilly, Fahad Khan, Rao Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
| Challenge: | a new bilingual medical mixture of experts LLM is designed for seamless interaction in both English and Arabic. |
| Approach: | They propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations. |
| Outcome: | The proposed model outperforms state-of-the-art medical LLMs in Arabic and Arabic . it outperformed the generic Arabic-English bilingual LLM, Jais-30B by 10% and 15% . |
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment (2025.coling-main)
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Umair Nawaz, Awais Muhammad, Hanan Gani, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan, Rao Anwer
| Challenge: | Recent studies have addressed this problem by building domain-specialized image-text data. |
| Approach: | They propose a vision-language foundational model dedicated to agriculture and livestock . they propose combining contrastive and self-supervised learning to learn fine-grained features . |
| Outcome: | The proposed model achieves 9.07% gain over standard CLIP training on 20 tasks. |