Modern Models, Medieval Texts: A POS Tagging Study of Old Occitan
This research addresses the challenge of processing low-resource historical languages for computational linguistics and historical language studies, but it is incremental as it evaluates existing models on new data without proposing novel methods.
The study tackled the problem of applying large language models (LLMs) to part-of-speech tagging for Old Occitan, a historical language with non-standardized orthography and variation, and found critical limitations in model performance when handling extreme variability.
Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing, yet their effectiveness in handling historical languages remains largely unexplored. This study examines the performance of open-source LLMs in part-of-speech (POS) tagging for Old Occitan, a historical language characterized by non-standardized orthography and significant diachronic variation. Through comparative analysis of two distinct corpora-hagiographical and medical texts-we evaluate how current models handle the inherent challenges of processing a low-resource historical language. Our findings demonstrate critical limitations in LLM performance when confronted with extreme orthographic and syntactic variability. We provide detailed error analysis and specific recommendations for improving model performance in historical language processing. This research advances our understanding of LLM capabilities in challenging linguistic contexts while offering practical insights for both computational linguistics and historical language studies.