<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chan-2026-transfer">
    <titleInfo>
        <title>Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Callum</namePart>
        <namePart type="family">Chan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Rachele</namePart>
            <namePart type="family">Sprugnoli</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Passarotti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">chan-2026-transfer</identifier>
    <identifier type="doi">10.63317/5eszd7yy3gfm</identifier>
    <location>
        <url>https://aclanthology.org/2026.lt4hala-1.22/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>234</start>
            <end>243</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting
%A Chan, Callum
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F chan-2026-transfer
%X 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.
%R 10.63317/5eszd7yy3gfm
%U https://aclanthology.org/2026.lt4hala-1.22/
%U https://doi.org/10.63317/5eszd7yy3gfm
%P 234-243

Markdown (Informal)

[Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting](https://aclanthology.org/2026.lt4hala-1.22/) (Chan, LT4HALA 2026)

• Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting (Chan, LT4HALA 2026)

ACL

• Callum Chan. 2026. Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting. In Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026, pages 234–243, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).