Papers by Omid Ghahroodi
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation (2025.findings-acl)
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Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani, Mohammadali Mohammadkhani, Bardia Mohammadi, Omid Ghahroodi, Mahdieh Soleymani Baghshah, Ehsaneddin Asgari
| Challenge: | Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. |
| Approach: | They review training strategies, robustness enhancements, loss functions, and agent-based approaches and outline open challenges and future directions to guide research in this evolving field. |
| Outcome: | The proposed model improves accuracy and accuracy while integrating external dynamic information for improved factual grounding. |
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment (2026.findings-eacl)
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Omid Ghahroodi, Arshia Hemmat, Marzia Nouri, Seyed Mohammad Hadi Hosseini, Doratossadat Dastgheib, Mohammad Vali Sanian, Alireza Sahebi, Reihaneh Zohrabi, Mohammad Hossein Rohban, Ehsaneddin Asgari, Mahdieh Soleymani Baghshah
| Challenge: | Recent advances in large vision-language models have primarily focused on English, with limited attention given to other languages. |
| Approach: | They propose a dataset to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. |
| Outcome: | The proposed model performs well across scientific reasoning, reasoning, and human-level understanding tasks in Persian and English. |
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models (2026.findings-acl)
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Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Ehsaneddin Asgari
| Challenge: | Existing evaluations focus on piecemeal or disconnected tasks, obscuring critical cognitive weaknesses and providing little insight for targeted improvement. |
| Approach: | They propose a bilingual, cognitively human-grounded multimodal benchmark for VLMs that evaluates six levels of cognition through carefully designed image–question–answer tasks. |
| Outcome: | The proposed framework ensures scalability, cultural inclusivity, and linguistic fidelity. |
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments (2024.lrec-main)
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Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian Heinrich, Nicolas Handke, Xiaoni Cai, Valentin Barriere, Doratossadat Dastgheib, Omid Ghahroodi, MohammadAli SadraeiJavaheri, Ehsaneddin Asgari, Lea Kawaletz, Henning Wachsmuth, Benno Stein
| Challenge: | Cultural norms can influence the prioritization of values, leading to distinct perspectives on debatable topics. |
| Approach: | They present a Touché23-ValueEval dataset that annotates 4780 new arguments and annotated 54 human values. |
| Outcome: | The Touché23-ValueEval dataset doubles the original Webis-ArgValués-22 dataset to 9324 arguments. |