Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting
arXiv:2608.04015v1 Announce Type: new Abstract: With the increase in digitized resources of Classical Latin texts and modern breakthroughs of Large Language Models (LLMs), I contribute to ancient language research by participating in EvaLatin 2026. This paper describes Team uOttawa's system description and results for the Named Entity Recognition (NER) shared task. The task is divided into two subtasks: coarse-grained NER with 11 classes and fine-grained NER with 28 classes, each evaluated under strict and fuzzy regimes. Through prompt engineering of commercial LLMs gemini-2.5-pro and claude-sonnet-4-5, I show that the underrepresented ancient Latin language can take advantage of cross-lingual transfer learning by using advancements made by the wider LLM development community. Overall, the methods discussed in this report demonstrate very strong results, placing first in both NER subtasks and achieving the best scores across all evaluation metrics and regimes among all submissions.
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[Submitted on 26 May 2026]
Title:Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting
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Abstract:With the increase in digitized resources of Classical Latin texts and modern breakthroughs of Large Language Models (LLMs), I contribute to ancient language research by participating in EvaLatin 2026. This paper describes Team uOttawa's system description and results for the Named Entity Recognition (NER) shared task. The task is divided into two subtasks: coarse-grained NER with 11 classes and fine-grained NER with 28 classes, each evaluated under strict and fuzzy regimes. Through prompt engineering of commercial LLMs gemini-2.5-pro and claude-sonnet-4-5, I show that the underrepresented ancient Latin language can take advantage of cross-lingual transfer learning by using advancements made by the wider LLM development community. Overall, the methods discussed in this report demonstrate very strong results, placing first in both NER subtasks and achieving the best scores across all evaluation metrics and regimes among all submissions.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2608.04015 [cs.CL]
(or arXiv:2608.04015v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.04015
arXiv-issued DOI via DataCite
Journal reference: EvaLatin (LT4HALA@LREC), ELRA, May 2026, Palma De Majorque, Spain
Submission history
From: Callum Chan [view email] [v1] Tue, 26 May 2026 23:37:49 UTC (62 KB)
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