mlsanityData to 14 Oct 2026

LITERA: An LLM Based Approach to Latin-to-English Translation

arXiv 2504.10660 ↗First posted 14 Apr 2025cs.CL cs.AIcited by 3

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Abstract (arXiv)

This paper introduces an LLM-based Latin-to-English translation platform designed to address the challenges of translating Latin texts. We named the model LITERA, which stands for Latin Interpretation and Translations into English for Research Assistance. Through a multi-layered translation process utilizing a fine-tuned version of GPT-4o-mini and GPT-4o, LITERA offers an unprecedented level of accuracy, showcased by greatly improved BLEU scores, particularly in classical Latin, along with improved BLEURT scores. The development of LITERA involved close collaboration with Duke University's Classical Studies Department, which was instrumental in creating a small, high-quality parallel Latin-English dataset. This paper details the architecture, fine-tuning methodology, and prompting strategies used in LITERA, emphasizing its ability to produce literal translations.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 14 Apr 2025.

Builds on

5 of 26 references.

References · 5

  1. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  2. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  3. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 438
  4. 2021Learning Compact Metrics for MTcited by 93
  5. 2021On the Difficulty of Translating Free-Order Case-Marking Languagescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.