@inproceedings{bove-etal-2026-evaluating,
title = "Evaluating the Effect of Prompt Language on {LLM}-based Translation: Evidence from {S}panish{\ensuremath{<}}{\ensuremath{>}}{I}talian Translation",
author = "Bove, Antonella and
Cataldo, Paola Di and
Maestroni, Davide",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.51/",
pages = "800--812",
ISBN = "9789403901411",
abstract = "The integration of large language models (LLMs) into translation practice has substantially reshaped translation workflows (Kornacki and Pietrzak, 2025). Since translation quality depends partly on how these models are prompted, prompt design deserves closer attention as a key stage of the LLM-augmented translation process. This study investigates Spanish{\ensuremath{<}}{\ensuremath{>}}Italian translation with GPT 5.1 in the advertising and biomedical domains. It examines whether prompt language affects the quality of translations generated with the GPT 5.1 model, and specifically whether prompts written in the target language outperform prompts written in English, the language most prevalent in the model{'}s training data (Armengol-Estap{\'e} et al., 2022). Three prompt templates, varying in complexity and informational content, were tested. The translations were first screened for textual similarity, and only the translations generated from the template that produced the greatest variation across outputs were subsequently selected for human evaluation. Human judgments were collected through a pairwise-comparison task. The findings indicate that prompts written in the target language tend to yield higher-quality translations compared to prompts written in English."
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<abstract>The integration of large language models (LLMs) into translation practice has substantially reshaped translation workflows (Kornacki and Pietrzak, 2025). Since translation quality depends partly on how these models are prompted, prompt design deserves closer attention as a key stage of the LLM-augmented translation process. This study investigates Spanish\ensuremath<\ensuremath>Italian translation with GPT 5.1 in the advertising and biomedical domains. It examines whether prompt language affects the quality of translations generated with the GPT 5.1 model, and specifically whether prompts written in the target language outperform prompts written in English, the language most prevalent in the model’s training data (Armengol-Estapé et al., 2022). Three prompt templates, varying in complexity and informational content, were tested. The translations were first screened for textual similarity, and only the translations generated from the template that produced the greatest variation across outputs were subsequently selected for human evaluation. Human judgments were collected through a pairwise-comparison task. The findings indicate that prompts written in the target language tend to yield higher-quality translations compared to prompts written in English.</abstract>
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%0 Conference Proceedings
%T Evaluating the Effect of Prompt Language on LLM-based Translation: Evidence from Spanish\ensuremath<\ensuremath>Italian Translation
%A Bove, Antonella
%A Cataldo, Paola Di
%A Maestroni, Davide
%Y Shterionov, Dimitar
%Y Vanmassenhove, Eva
%Y De Sisto, Mirella
%Y Blain, Fred
%Y Pourmostafa Roshan Sharami, Javad
%Y Lepp, Lisa
%Y Manna, Chiara
%Y Rescigno, Argentina Anna
%Y Karakanta, Alina
%Y Rigouts Terryn, Ayla
%Y Lardelli, Manuel
%Y Resende, Natalia
%Y Murgolo, Elena
%Y Hackenbuchner, Janiça
%Y Zaretskaya, Anna
%Y Esplà-Gomis, Miquel
%Y Etchegoyhen, Thierry
%Y Gromann, Dagmar
%Y Bawden, Rachel
%Y Haddow, Barry
%Y Szoc, Sara
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, The Netherlands
%@ 9789403901411
%F bove-etal-2026-evaluating
%X The integration of large language models (LLMs) into translation practice has substantially reshaped translation workflows (Kornacki and Pietrzak, 2025). Since translation quality depends partly on how these models are prompted, prompt design deserves closer attention as a key stage of the LLM-augmented translation process. This study investigates Spanish\ensuremath<\ensuremath>Italian translation with GPT 5.1 in the advertising and biomedical domains. It examines whether prompt language affects the quality of translations generated with the GPT 5.1 model, and specifically whether prompts written in the target language outperform prompts written in English, the language most prevalent in the model’s training data (Armengol-Estapé et al., 2022). Three prompt templates, varying in complexity and informational content, were tested. The translations were first screened for textual similarity, and only the translations generated from the template that produced the greatest variation across outputs were subsequently selected for human evaluation. Human judgments were collected through a pairwise-comparison task. The findings indicate that prompts written in the target language tend to yield higher-quality translations compared to prompts written in English.
%U https://aclanthology.org/2026.eamt-1.51/
%P 800-812
Markdown (Informal)
[Evaluating the Effect of Prompt Language on LLM-based Translation: Evidence from Spanish<>Italian Translation](https://aclanthology.org/2026.eamt-1.51/) (Bove et al., EAMT 2026)
ACL