从传统标注器到LLMs：一种中世纪罗曼语词性标注的比较研究
From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages
AI总结：
本文比较了传统规则和统计标注器与现代开源LLMs在中世纪罗曼语词性标注中的表现，发现LLMs在零样本、少样本、单语微调和跨语言迁移学习中均表现更优，尤其在资源匮乏语言中跨语言迁移学习效果显著。
摘要：
中世纪罗曼语的词性标注因拼写变异、形态复杂性和注释资源有限而具有挑战性。本文系统评估了大型语言模型（LLMs）在三种中世纪变体（中世纪奥克语、中世纪加泰罗尼亚语和中世纪法语）上的词性标注性能。我们比较了传统基于规则和统计的标注器与现代开源LLMs，在零样本提示、少样本提示、单语微调和跨语言迁移学习设置下。实验表明，LLM方法在零样本和少样本设置中均优于传统标注器，微调和多语言训练带来最大提升。特别是跨语言迁移学习在资源匮乏语言中效果显著，而针对双语训练可超越更广泛的多语言配置。结果强调了在历史NLP中设计迁移策略时语言亲疏关系和数据集特征的重要性。这些发现提供了现代神经方法应用于中世纪文本处理的实证见解，并为数字人文研究中部署LLM词性标注管道提供了实用指导。所有代码、模型和处理数据集均公开以确保可重复性。
英文摘要：
Part-of-speech (POS) tagging for Medieval Romance languages remains challenging due to orthographic variation, morphological complexity, and limited annotated resources. This paper presents a systematic empirical evaluation of large language models (LLMs) for POS tagging across three medieval varieties: Medieval Occitan, Medieval Catalan, and Medieval French. We compare traditional rule-based and statistical taggers with modern open-source LLMs under zero-shot prompting, few-shot prompting, monolingual fine-tuning, and cross-lingual transfer learning settings. Experiments on historically grounded datasets show that LLM-based approaches consistently outperform traditional taggers, with fine-tuning and multilingual training yielding the largest improvements. In particular, cross-lingual transfer learning substantially benefits under-resourced varieties, while targeted bilingual training can outperform broader multilingual configurations for specific target languages. The results highlight the importance of linguistic proximity and dataset characteristics when designing transfer strategies for historical NLP. These findings provide empirical insights into the applicability of modern neural methods to medieval text processing and provide practical guidance for deploying LLM-based POS tagging pipelines in digital humanities research. All code, models, and processed datasets are released for reproducibility.
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