# Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models

arXiv: https://arxiv.org/abs/2603.16718 · HTML: https://mlsanity.com/paper/2603.16718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [Prompting Language Models for Linguistic Structure](https://mlsanity.com/paper/2211.07830/) (2022)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Evaluating Morphological Compositional Generalization in Large Language Models](https://mlsanity.com/paper/2410.12656/) (2024)
- [Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects](https://mlsanity.com/paper/2110.06852/) (2021)
- [Can we teach language models to gloss endangered languages?](https://mlsanity.com/paper/2406.18895/) (2024)
- [Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging](https://mlsanity.com/paper/1910.02267/) (2019)
- [LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual](https://mlsanity.com/paper/1309.5652/) (2013)
- [Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling](https://mlsanity.com/paper/1910.12702/) (2019)
- [Self-Correction Makes LLMs Better Parsers](https://mlsanity.com/paper/2504.14165/) (2025)
- [Better Benchmarking LLMs for Zero-Shot Dependency Parsing](https://mlsanity.com/paper/2502.20866/) (2025)
- [Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs](https://mlsanity.com/paper/2506.09983/) (2025)

## Abstract (arXiv)

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.