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        <title>Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models</title>
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    <abstract>LLMs perform strongly across NLP, but their ability to produce explicit grammatical analyses remains unclear. Arabic provides a challenging testbed due to its rich morphology and orthographic ambiguity, which create strong morphology–syntax interactions. We present a unified evaluation of LLMs on Arabic morphosyntactic tagging and dependency parsing, covering pre-tokenized, raw-text, and cascaded settings. We compare zero-shot prompting with retrieval-based in-context learning. Relevant demonstrations substantially improve performance. The strongest LLMs approach supervised tagging and parsing systems; however, they require substantial annotated data for demonstration retrieval and considerable computational resources. We make all code and data used in this paper publicly available.</abstract>
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%0 Conference Proceedings
%T Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models
%A Adel, Mohamed
%A Alhafni, Bashar
%A Habash, Nizar
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F adel-etal-2026-arabic
%X LLMs perform strongly across NLP, but their ability to produce explicit grammatical analyses remains unclear. Arabic provides a challenging testbed due to its rich morphology and orthographic ambiguity, which create strong morphology–syntax interactions. We present a unified evaluation of LLMs on Arabic morphosyntactic tagging and dependency parsing, covering pre-tokenized, raw-text, and cascaded settings. We compare zero-shot prompting with retrieval-based in-context learning. Relevant demonstrations substantially improve performance. The strongest LLMs approach supervised tagging and parsing systems; however, they require substantial annotated data for demonstration retrieval and considerable computational resources. We make all code and data used in this paper publicly available.
%U https://aclanthology.org/2026.emnlp-main.1467/
%P 33582-33604

Markdown (Informal)

[Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models](https://aclanthology.org/2026.emnlp-main.1467/) (Adel et al., EMNLP 2026)

• Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models (Adel et al., EMNLP 2026)

ACL

• Mohamed Adel, Bashar Alhafni, and Nizar Habash. 2026. Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 33582–33604, Budapest, Hungary. Association for Computational Linguistics.