Papers by Seyed Abolghasem Mirroshandel
Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground (2024.findings-acl)
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Adil Soubki, John Murzaku, Arash Yousefi Jordehi, Peter Zeng, Magdalena Markowska, Seyed Abolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing benchmarks for theory of mind (ToM) use synthetic data, which can misalign with human behavior. |
| Approach: | They propose a question-answer benchmark based on naturally occurring spoken dialogs to evaluate theory of mind capabilities of language models. |
| Outcome: | The proposed dataset shows that LMs struggle to demonstrate theory of mind (ToM) . |
RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems (2023.emnlp-demo)
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Yasaman Boreshban, Seyed Morteza Mirbostani, Seyedeh Fatemeh Ahmadi, Gita Shojaee, Fatemeh Kamani, Gholamreza Ghassem-Sani, Seyed Abolghasem Mirroshandel
| Challenge: | Question answering (QA) systems have reached human-level accuracy, but they are not robust enough and vulnerable to adversarial examples. |
| Approach: | They modified the attack algorithms widely used in text classification to fit them for QA systems. |
| Outcome: | The proposed framework is the first open-source toolkit for investigating textual adversarial attacks in QA systems. |
Active Few-Shot Learning for Text Classification (2025.naacl-long)
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Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, Seyed Abolghasem Mirroshandel, Owen Rambow, Cornelia Caragea
| Challenge: | Recent advances in Large Language Models (LLMs) have boosted the use of Few-Shot Learning (FSL) methods in natural language processing. |
| Approach: | They propose a method that identifies effective support instances from the unlabeled pool and can work with different LLMs. |
| Outcome: | The proposed method improves on five tasks on which it is tested on five LLMs. |