@inproceedings{signoroni-rychly-2026-llms,
title = "Can {LLM}s Translate {I}taly{'}s Language Varieties?",
author = "Signoroni, Edoardo and
Rychl{\'y}, Pavel",
editor = "Ojha, Atul Kr. and
Liu, Chao-hong and
Vylomova, Ekaterina and
Pirinen, Flammie and
Washington, Jonathan and
Oco, Nathaniel and
Zhao, Xiaobing",
booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages ({L}o{R}es{MT} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.loresmt-1.5/",
pages = "69--77",
ISBN = "979-8-89176-366-1",
abstract = "We evaluate the capabilities of several small large language models (LLMs) to translate between Italian and six low-resource language varieties from Italy (Friulan, Ligurian, Lombard, Sicilian, Sardinian, and Venetian). Using recent benchmark datasets, such as FLORES+ and OLDI-Seed, we compare prompting and fine-tuning approaches for downstream translation, evaluated with CHRF scores. Our findings confirm that these LLMs struggle to translate into and from these low-resource language varieties. Pretraining and fine-tuning a small LLM did not yield improvements over a zero-shot baseline. These results underscore the need for further NLP research on Italy{'}s low-resource language varieties. As the digital divide continues to threaten the conservation of this diverse linguistic landscape, greater engagement with speaker communities to create better and more representative datasets is essential to boost the translation performance of current LLMs."
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<abstract>We evaluate the capabilities of several small large language models (LLMs) to translate between Italian and six low-resource language varieties from Italy (Friulan, Ligurian, Lombard, Sicilian, Sardinian, and Venetian). Using recent benchmark datasets, such as FLORES+ and OLDI-Seed, we compare prompting and fine-tuning approaches for downstream translation, evaluated with CHRF scores. Our findings confirm that these LLMs struggle to translate into and from these low-resource language varieties. Pretraining and fine-tuning a small LLM did not yield improvements over a zero-shot baseline. These results underscore the need for further NLP research on Italy’s low-resource language varieties. As the digital divide continues to threaten the conservation of this diverse linguistic landscape, greater engagement with speaker communities to create better and more representative datasets is essential to boost the translation performance of current LLMs.</abstract>
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%0 Conference Proceedings
%T Can LLMs Translate Italy’s Language Varieties?
%A Signoroni, Edoardo
%A Rychlý, Pavel
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-366-1
%F signoroni-rychly-2026-llms
%X We evaluate the capabilities of several small large language models (LLMs) to translate between Italian and six low-resource language varieties from Italy (Friulan, Ligurian, Lombard, Sicilian, Sardinian, and Venetian). Using recent benchmark datasets, such as FLORES+ and OLDI-Seed, we compare prompting and fine-tuning approaches for downstream translation, evaluated with CHRF scores. Our findings confirm that these LLMs struggle to translate into and from these low-resource language varieties. Pretraining and fine-tuning a small LLM did not yield improvements over a zero-shot baseline. These results underscore the need for further NLP research on Italy’s low-resource language varieties. As the digital divide continues to threaten the conservation of this diverse linguistic landscape, greater engagement with speaker communities to create better and more representative datasets is essential to boost the translation performance of current LLMs.
%U https://aclanthology.org/2026.loresmt-1.5/
%P 69-77
Markdown (Informal)
[Can LLMs Translate Italy’s Language Varieties?](https://aclanthology.org/2026.loresmt-1.5/) (Signoroni & Rychlý, LoResMT 2026)
ACL
- Edoardo Signoroni and Pavel Rychlý. 2026. Can LLMs Translate Italy’s Language Varieties?. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 69–77, Rabat, Morocco. Association for Computational Linguistics.