Papers by Mahdieh Soleymani Baghshah
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation (2025.findings-acl)
Copied to clipboard
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| Challenge: | Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data.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. |
Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy (2026.findings-acl)
Copied to clipboard
Hosein Hasani, Mohammadali Banayeeanzade, Ali Nafisi, Sadegh Mohammadian, Fatemeh Askari, Mobin Bagherian, Amirmohammad Izadi, Mahdieh Soleymani Baghshah
| Challenge: | Large language models exhibit systematic limitations in counting tasks due to depth constraints. |
| Approach: | They propose a method that decomposes large counting tasks into smaller, independent sub-problems that the model can reliably solve. |
| Outcome: | The proposed method surpasses architectural limitations and achieves higher accuracy on large-scale counting tasks. |
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment (2026.findings-eacl)
Copied to clipboard
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. |
CER: Confidence Enhanced Reasoning in LLMs (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to enhance the reliability of Large Language Models (LLMs) in complex reasoning tasks are limited by their limitations. |
| Approach: | They propose an uncertainty-aware framework to enhance the reliability of Large Language Models . they quantify the confidence of intermediate answers and evaluate the reliability based on these confidences a way that reflects the reliability. |
| Outcome: | The proposed approach improves accuracy of large language models in math and open-domain tasks by 7.4% and 5.8% over baseline approaches. |