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University of Ottawa wins Latin NER task using Gemini and Claude LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers from the University of Ottawa have achieved top results in the EvaLatin 2026 Named Entity Recognition (NER) shared task for Classical Latin. By employing prompt engineering with large language models Gemini 2.5 Pro and Claude Sonnet 4.5, they demonstrated the effectiveness of cross-lingual transfer learning for underrepresented ancient languages. Their system secured first place in both coarse-grained and fine-grained NER subtasks, outperforming all other submissions across various evaluation metrics. AI

IMPACT Demonstrates LLM capabilities for low-resource languages, potentially accelerating digital humanities research.

RANK_REASON Academic paper detailing research results on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

University of Ottawa wins Latin NER task using Gemini and Claude LLMs

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Academic paper detailing research results on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Callum Chan ·

    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 uOtta…

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