用大模型实现精准拉丁文到英文翻译，助力古典研究。
LITERA: An LLM Based Approach to Latin-to-English Translation
- 基于微调的GPT-4o-mini与GPT-4o构建多层翻译流程。
- 在古典拉丁文中实现显著提升的BLEU与BLEURT得分。
- 专为学术研究设计，适合古典学与语言学学者使用。
本文提出一种基于大模型的拉丁文到英文翻译平台LITERA（拉丁语解释与研究辅助英文翻译），旨在解决拉丁文翻译难题。通过结合微调的GPT-4o-mini与GPT-4o，采用多层翻译流程，LITERA在古典拉丁文翻译中实现了显著更高的BLEU分数和改进的BLEURT评分。项目与杜克大学古典学系紧密合作，构建了一个小型高质量平行拉丁-英文语料库。本文详述了LITERA的架构、微调方法与提示策略，强调其生成直译文本的能力，适用于学术研究场景。
原文摘要 · Abstract (English)
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.
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