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Benchmarking Arabic Authorship Attribution and Style Transfer with Large Language Models
Hamed, Injy Alhafni, Bashar Habash, Nizar Solorio, Thamar
Hamed, Injy
Alhafni, Bashar
Habash, Nizar
Solorio, Thamar
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benchmarking_arabic.pdf
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Natural Language Processing
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Conference proceeding
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http://creativecommons.org/licenses/by/4.0/
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Abstract
Writing style is a fundamental component of natural language. However, significant research gaps remain in two key style-centric tasks: authorship attribution (AA) and authorship style transfer, particularly for Arabic. In this work, we revisit both tasks in that context. We introduce a new AA dataset comprising texts in Modern Standard and Dialectal Arabic. We train transformer-based AA models using dual cross-entropy and contrastive learning loss objectives, and validate model performance through human evaluation. We then utilize the trained AA model to benchmark a range of large language models (LLMs) on style recognition and generation tasks, providing new insights into their capabilities in modeling Arabic writing styles. Our work reveals limitations of current models and provides resources to advance research in this direction.
Citation
I. Hamed, B. Alhafni, N. Habash, T. Solorio, "Benchmarking Arabic Authorship Attribution and Style Transfer with Large Language Models," 2026, pp. 7262-7278.
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Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)
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The Fifteenth Language Resources and Evaluation Conference (LREC 2026)
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The Fifteenth Language Resources and Evaluation Conference (LREC 2026)
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ELDA (Evaluations and Language resources Distribution Agency)