Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models

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.

Computation and Language (cs.CL)

arXiv:2603.16718 [cs.CL]

(or arXiv:2603.16718v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.16718 Focus to learn more arXiv-issued DOI via DataCite

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