@inproceedings{volk-etal-2024-llm,
title = "{LLM}-based Machine Translation and Summarization for {L}atin",
author = {Volk, Martin and
Fischer, Dominic Philipp and
Fischer, Lukas and
Scheurer, Patricia and
Str{\"o}bel, Phillip Benjamin},
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lt4hala-1.15",
pages = "122--128",
abstract = "This paper presents an evaluation of machine translation for Latin. We tested multilingual Large Language Models, in particular GPT-4, on letters from the 16th century that are in Latin and Early New High German. Our experiments include translation and cross-language summarization for the two historical languages into modern English and German. We show that LLM-based translation for Latin is clearly superior to previous approaches. We also show that LLM-based paraphrasing of Latin paragraphs from the historical letters produces English and German summaries that are close to human summaries published in the edition.",
}
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<abstract>This paper presents an evaluation of machine translation for Latin. We tested multilingual Large Language Models, in particular GPT-4, on letters from the 16th century that are in Latin and Early New High German. Our experiments include translation and cross-language summarization for the two historical languages into modern English and German. We show that LLM-based translation for Latin is clearly superior to previous approaches. We also show that LLM-based paraphrasing of Latin paragraphs from the historical letters produces English and German summaries that are close to human summaries published in the edition.</abstract>
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%0 Conference Proceedings
%T LLM-based Machine Translation and Summarization for Latin
%A Volk, Martin
%A Fischer, Dominic Philipp
%A Fischer, Lukas
%A Scheurer, Patricia
%A Ströbel, Phillip Benjamin
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F volk-etal-2024-llm
%X This paper presents an evaluation of machine translation for Latin. We tested multilingual Large Language Models, in particular GPT-4, on letters from the 16th century that are in Latin and Early New High German. Our experiments include translation and cross-language summarization for the two historical languages into modern English and German. We show that LLM-based translation for Latin is clearly superior to previous approaches. We also show that LLM-based paraphrasing of Latin paragraphs from the historical letters produces English and German summaries that are close to human summaries published in the edition.
%U https://aclanthology.org/2024.lt4hala-1.15
%P 122-128
Markdown (Informal)
[LLM-based Machine Translation and Summarization for Latin](https://aclanthology.org/2024.lt4hala-1.15) (Volk et al., LT4HALA-WS 2024)
ACL
- Martin Volk, Dominic Philipp Fischer, Lukas Fischer, Patricia Scheurer, and Phillip Benjamin Ströbel. 2024. LLM-based Machine Translation and Summarization for Latin. In Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024, pages 122–128, Torino, Italia. ELRA and ICCL.