Papers by Sara Bourbour Hosseinbeigi
Matina: A Large-Scale 73B Token Persian Text Corpus (2025.naacl-long)
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Sara Bourbour Hosseinbeigi, Fatemeh Taherinezhad, Heshaam Faili, Hamed Baghbani, Fatemeh Nadi, Mostafa Amiri
| Challenge: | Existing Persian datasets are small and lack content diversity . lack of high-quality data has slowed development of NLP models and open-source LLMs for Persian. |
| Approach: | They propose a Persian dataset of 72.9B tokens that is preprocessed and deduplicated to ensure high data quality. |
| Outcome: | The proposed model performs well on key Persian NLP tasks. |
Advancing Persian LLM Evaluation (2025.findings-naacl)
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Sara Bourbour Hosseinbeigi, Behnam Rohani, Mostafa Masoudi, Mehrnoush Shamsfard, Zahra Saaberi, Mostafa Karimi Manesh, Mohammad Amin Abbasi
| Challenge: | Existing evaluation approaches for large language models in low-resource languages like Persian lack comprehensive frameworks, limiting their ability to assess models’ performance over a wide range of tasks requiring considerable cultural and contextual knowledge. |
| Approach: | They propose to provide two new benchmarks to assess models' performance over a wide range of tasks requiring considerable cultural and contextual knowledge. |
| Outcome: | The proposed benchmarks challenge the current state-of-the-art models’ abilities in a variety of Persian language comprehension tasks while reducing data contamination while providing an accurate assessment of Persian LLMs. |
Matina: A Culturally-Aligned Persian Language Model Using Multiple LoRA Experts (2025.findings-acl)
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Sara Bourbour Hosseinbeigi, MohammadAli SeifKashani, Javad Seraj, Fatemeh Taherinezhad, Ali Nafisi, Fatemeh Nadi, Iman Barati, Hosein Hasani, Mostafa Amiri, Mostafa Masoudi
| Challenge: | Existing Large language models fail to accurately model underrepresented languages and cultures, limiting their applicability and acceptance. |
| Approach: | They develop a Persian-focused multi-expert model that incorporates Iranian cultural values and linguistic structures. |
| Outcome: | The proposed model outperforms baseline models in task performance and user satisfaction. |