@inproceedings{simplicio-etal-2026-amalia,
title = "{AMALIA}: A Fully Open Large Language Model for {E}uropean {P}ortuguese",
author = "Simpl{\'i}cio, Afonso and
Vinagre, Gon{\c{c}}alo and
Ramos, Miguel Moura and
Tavares, Diogo and
Ferreira, Rafael and
Attanasio, Giuseppe and
Alves, Duarte M. and
Calvo, In{\^e}s and
Vieira, In{\^e}s and
Guerra, Rui and
Furtado, James and
Canaverde, Beatriz and
Paulo, Iago and
Ramos, Vasco and
Gl{\'o}ria-Silva, Diogo and
Faria, Miguel and
Treviso, Marcos and
Gomes, Daniel and
Gomes, Pedro and
Semedo, David and
Martins, Andr{\'e} and
Magalh{\~a}es, Jo{\~a}o",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.38/",
pages = "380--391",
ISBN = "979-8-89176-387-6",
abstract = "Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant{'}s linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a suite of pt-PT benchmarks that includes translated standard tasks and four new datasets targeting pt-PT generation, linguistic competence, and pt-PT/pt-BR bias. Experiments show that AMALIA matches strong baselines on translated benchmarks while substantially improving performance on pt-PT-specific evaluations, supporting the case for targeted training and native benchmarking for European Portuguese."
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<abstract>Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant’s linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a suite of pt-PT benchmarks that includes translated standard tasks and four new datasets targeting pt-PT generation, linguistic competence, and pt-PT/pt-BR bias. Experiments show that AMALIA matches strong baselines on translated benchmarks while substantially improving performance on pt-PT-specific evaluations, supporting the case for targeted training and native benchmarking for European Portuguese.</abstract>
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%0 Conference Proceedings
%T AMALIA: A Fully Open Large Language Model for European Portuguese
%A Simplício, Afonso
%A Vinagre, Gonçalo
%A Ramos, Miguel Moura
%A Tavares, Diogo
%A Ferreira, Rafael
%A Attanasio, Giuseppe
%A Alves, Duarte M.
%A Calvo, Inês
%A Vieira, Inês
%A Guerra, Rui
%A Furtado, James
%A Canaverde, Beatriz
%A Paulo, Iago
%A Ramos, Vasco
%A Glória-Silva, Diogo
%A Faria, Miguel
%A Treviso, Marcos
%A Gomes, Daniel
%A Gomes, Pedro
%A Semedo, David
%A Martins, André
%A Magalhães, João
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F simplicio-etal-2026-amalia
%X Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant’s linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a suite of pt-PT benchmarks that includes translated standard tasks and four new datasets targeting pt-PT generation, linguistic competence, and pt-PT/pt-BR bias. Experiments show that AMALIA matches strong baselines on translated benchmarks while substantially improving performance on pt-PT-specific evaluations, supporting the case for targeted training and native benchmarking for European Portuguese.
%U https://aclanthology.org/2026.propor-1.38/
%P 380-391
Markdown (Informal)
[AMALIA: A Fully Open Large Language Model for European Portuguese](https://aclanthology.org/2026.propor-1.38/) (Simplício et al., PROPOR 2026)
ACL
- Afonso Simplício, Gonçalo Vinagre, Miguel Moura Ramos, Diogo Tavares, Rafael Ferreira, Giuseppe Attanasio, Duarte M. Alves, Inês Calvo, Inês Vieira, Rui Guerra, James Furtado, Beatriz Canaverde, Iago Paulo, Vasco Ramos, Diogo Glória-Silva, Miguel Faria, Marcos Treviso, Daniel Gomes, Pedro Gomes, David Semedo, André Martins, and João Magalhães. 2026. AMALIA: A Fully Open Large Language Model for European Portuguese. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 380–391, Salvador, Brazil. Association for Computational Linguistics.