用大模型搞定阿拉伯语形态句法标注与依存分析，效果接近专用模型。
Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models
- 通过提示工程与上下文学习，利用阿拉伯树库示例提升性能。
- 专有模型在词性标注上接近监督基线，依存分析也具备竞争力。
- 适合研究阿拉伯语自然语言处理或大模型结构生成能力的学者。
大型语言模型（LLMs）在众多NLP任务中表现优异，但其生成显式语言结构的能力尚不明确。本文评估了指令微调的LLMs在标准阿拉伯语上的两个结构化预测任务：形态句法标注和带标签依存句法分析。阿拉伯语因丰富的形态变化和拼写歧义，形成强烈的形态-句法交互，是极具挑战性的测试场景。我们比较了零样本提示与基于检索的上下文学习（ICL），使用来自阿拉伯语树库的示例进行演示。结果表明，提示设计和示例选择对性能影响显著：专有模型在特征级标注上接近监督基线，在依存分析上也达到与专用解析器相当的水平。在原始文本设置下，分词仍是难点，但基于检索的ICL能同时改善分词与依存分析效果。分析揭示了LLMs在阿拉伯语形态句法与句法结构捕捉中的可靠点与困难点。
原文摘要 · Abstract (English)
Large language models (LLMs) perform strongly on many NLP tasks, but their ability to produce explicit linguistic structure remains unclear. We evaluate instruction-tuned LLMs on two structured prediction tasks for Standard Arabic: morphosyntactic tagging and labeled dependency parsing. Arabic provides a challenging testbed due to its rich morphology and orthographic ambiguity, which create strong morphology-syntax interactions. We compare zero-shot prompting with retrieval-based in-context learning (ICL) using examples from Arabic treebanks. Results show that prompt design and demonstration selection strongly affect performance: proprietary models approach supervised baselines for feature-level tagging and become competitive with specialized dependency parsers. In raw-text settings, tokenization remains challenging, though retrieval-based ICL improves both parsing and tokenization. Our analysis highlights which aspects of Arabic morphosyntax and syntax LLMs capture reliably and which remain difficult.
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