Papers by Gagan Bhatia
Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks (2024.acl-long)
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Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, Abdelrahman Mohamed, Muhammad Abdul-Mageed
| Challenge: | MLLMs have proven effective in a wide range of tasks that require complex reasoning and linguistic comprehension, but they are limited to English-based settings. |
| Approach: | They propose a family of Arabic multimodal large language models with strong vision and language capabilities. |
| Outcome: | The proposed models show strong performance on visual reasoning tasks and language capabilities. |
DateLogicQA: Benchmarking Temporal Biases in Large Language Models (2025.naacl-srw)
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| Challenge: | DateLogicQA examines temporal biases in Large Language Models (LLMs) 190 questions are curated by humans to examine temporal reasoning across date formats and contexts . |
| Approach: | They propose a human-curated benchmark of 190 questions specifically designed to understand temporal bias in Large Language Models. |
| Outcome: | The proposed dataset covers seven date formats across past, present, and future contexts . it examines four reasoning types: commonsense, factual, conceptual, and numerical . |
From RAG to Agentic RAG for Faithful Islamic Question Answering (2026.findings-acl)
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Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar, George Mikros, Fadi Zaraket, Mahmoud Alhirthani, Mutaz al-Khatib, Logan Cochrane, Kareem Mohamed Darwish, Rashid Yahiaoui, Firoj Alam
| Challenge: | Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. |
| Approach: | They propose a bilingual, bilingual, Arabic/English benchmark with atomic single-gold answers that measures hallucination and abstention. |
| Outcome: | The proposed model improves accuracy and robustness even with a small model. |
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks (2025.findings-naacl)
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Gagan Bhatia, El Moatez Billah Nagoudi, Abdellah El Mekki, Fakhraddin Alwajih, Muhammad Abdul-Mageed
| Challenge: | In this paper, we introduce a family of embedding models addressing both small-scale and large-scale use cases. |
| Approach: | They propose to use ArabicMTEB to evaluate Arabic text embedding models . they propose to build a benchmark suite that assesses cross-lingual, multi-dialectal, multidomain, and multi-cultural Arabic text embedded models. |
| Outcome: | The proposed models outperform Multilingual-E5-large and Swan-Large in most Arabic tasks while remaining dialectally and culturally aware. |
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. |
FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models (2024.findings-acl)
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| Challenge: | FinTral is a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. |
| Approach: | They introduce FinTral, a suite of state-of-the-art multimodal large language models built upon the Mistral-7b model and tailored for financial analysis. |
| Outcome: | The proposed model outperforms ChatGPT-3.5 and GPT-4 in five out of nine tasks and surpasses GPT-4.5 in five of nine task evaluations. |
Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning (2025.emnlp-main)
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| Challenge: | a tokeniser that splits "2025-03-14" into "20", "25", "-0", "3", "-1", "4" obscures temporal cues and obscures structure . excessive fragmentation correlates with accuracy drops of up to 10 points on uncommon dates . |
| Approach: | They propose a date fragmentation ratio measure that measures how faithfully a tokeniser preserves multi-digit date components. |
| Outcome: | The proposed method shows that excessive fragmentation correlates with accuracy drops of up to 10 points on uncommon dates like historical and futuristic dates. |