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Abstract
While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the first study of Arabic KE. We evaluate four methods (ROME, MEMIT, ICE, and LTE) on Arabic translations of the ZsRE and Counterfact benchmarks, analyzing both multilingual and cross-lingual settings. Our experiments on Llama-2-7B-chat show show that parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. We extend Learning-To-Edit (LTE) to a multilingual setting and show that joint Arabic-English training improves both editability and transfer. We release Arabic KE benchmarks and multilingual training for LTE data to support future research.- Anthology ID:
- 2025.arabicnlp-main.34
- Volume:
- Proceedings of The Third Arabic Natural Language Processing Conference
- Month:
- November
- Year:
- 2025
- Address:
- Suzhou, China
- Editors:
- Kareem Darwish, Ahmed Ali, Ibrahim Abu Farha, Samia Touileb, Imed Zitouni, Ahmed Abdelali, Sharefah Al-Ghamdi, Sakhar Alkhereyf, Wajdi Zaghouani, Salam Khalifa, Badr AlKhamissi, Rawan Almatham, Injy Hamed, Zaid Alyafeai, Areeb Alowisheq, Go Inoue, Khalil Mrini, Waad Alshammari
- Venue:
- ArabicNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 417–424
- Language:
- URL:
- https://aclanthology.org/2025.arabicnlp-main.34/
- DOI:
- Bibkey:
- Cite (ACL):
- Basel Mousi, Nadir Durrani, and Fahim Dalvi. 2025. An Exploration of Knowledge Editing for Arabic. In Proceedings of The Third Arabic Natural Language Processing Conference, pages 417–424, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- An Exploration of Knowledge Editing for Arabic (Mousi et al., ArabicNLP 2025)
- Copy Citation:
- PDF:
- https://aclanthology.org/2025.arabicnlp-main.34.pdf
Export citation
@inproceedings{mousi-etal-2025-exploration,
title = "An Exploration of Knowledge Editing for {A}rabic",
author = "Mousi, Basel and
Durrani, Nadir and
Dalvi, Fahim",
editor = "Darwish, Kareem and
Ali, Ahmed and
Abu Farha, Ibrahim and
Touileb, Samia and
Zitouni, Imed and
Abdelali, Ahmed and
Al-Ghamdi, Sharefah and
Alkhereyf, Sakhar and
Zaghouani, Wajdi and
Khalifa, Salam and
AlKhamissi, Badr and
Almatham, Rawan and
Hamed, Injy and
Alyafeai, Zaid and
Alowisheq, Areeb and
Inoue, Go and
Mrini, Khalil and
Alshammari, Waad",
booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.arabicnlp-main.34/",
pages = "417--424",
ISBN = "979-8-89176-352-4",
abstract = "While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the first study of Arabic KE. We evaluate four methods (ROME, MEMIT, ICE, and LTE) on Arabic translations of the ZsRE and Counterfact benchmarks, analyzing both multilingual and cross-lingual settings. Our experiments on Llama-2-7B-chat show show that parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. We extend Learning-To-Edit (LTE) to a multilingual setting and show that joint Arabic-English training improves both editability and transfer. We release Arabic KE benchmarks and multilingual training for LTE data to support future research."
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%0 Conference Proceedings %T An Exploration of Knowledge Editing for Arabic %A Mousi, Basel %A Durrani, Nadir %A Dalvi, Fahim %Y Darwish, Kareem %Y Ali, Ahmed %Y Abu Farha, Ibrahim %Y Touileb, Samia %Y Zitouni, Imed %Y Abdelali, Ahmed %Y Al-Ghamdi, Sharefah %Y Alkhereyf, Sakhar %Y Zaghouani, Wajdi %Y Khalifa, Salam %Y AlKhamissi, Badr %Y Almatham, Rawan %Y Hamed, Injy %Y Alyafeai, Zaid %Y Alowisheq, Areeb %Y Inoue, Go %Y Mrini, Khalil %Y Alshammari, Waad %S Proceedings of The Third Arabic Natural Language Processing Conference %D 2025 %8 November %I Association for Computational Linguistics %C Suzhou, China %@ 979-8-89176-352-4 %F mousi-etal-2025-exploration %X While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the first study of Arabic KE. We evaluate four methods (ROME, MEMIT, ICE, and LTE) on Arabic translations of the ZsRE and Counterfact benchmarks, analyzing both multilingual and cross-lingual settings. Our experiments on Llama-2-7B-chat show show that parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. We extend Learning-To-Edit (LTE) to a multilingual setting and show that joint Arabic-English training improves both editability and transfer. We release Arabic KE benchmarks and multilingual training for LTE data to support future research. %U https://aclanthology.org/2025.arabicnlp-main.34/ %P 417-424
Markdown (Informal)
[An Exploration of Knowledge Editing for Arabic](https://aclanthology.org/2025.arabicnlp-main.34/) (Mousi et al., ArabicNLP 2025)
- An Exploration of Knowledge Editing for Arabic (Mousi et al., ArabicNLP 2025)
ACL
- Basel Mousi, Nadir Durrani, and Fahim Dalvi. 2025. An Exploration of Knowledge Editing for Arabic. In Proceedings of The Third Arabic Natural Language Processing Conference, pages 417–424, Suzhou, China. Association for Computational Linguistics.