@inproceedings{santos-etal-2024-advancing,
title = "Advancing Generative {AI} for {P}ortuguese with Open Decoder Gerv{\'a}sio {PT}*",
author = "Santos, Rodrigo and
Silva, Jo{\~a}o Ricardo and
Gomes, Lu{\'\i}s and
Rodrigues, Jo{\~a}o and
Branco, Ant{\'o}nio",
editor = "Melero, Maite and
Sakti, Sakriani and
Soria, Claudia",
booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.sigul-1.3",
pages = "16--26",
abstract = "To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gerv{\'a}sio PT*, a strong LLaMA 2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gerv{\'a}sio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.",
}
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<abstract>To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gervásio PT*, a strong LLaMA 2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gervásio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.</abstract>
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%0 Conference Proceedings
%T Advancing Generative AI for Portuguese with Open Decoder Gervásio PT*
%A Santos, Rodrigo
%A Silva, João Ricardo
%A Gomes, Luís
%A Rodrigues, João
%A Branco, António
%Y Melero, Maite
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F santos-etal-2024-advancing
%X To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gervásio PT*, a strong LLaMA 2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gervásio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.
%U https://aclanthology.org/2024.sigul-1.3
%P 16-26
Markdown (Informal)
[Advancing Generative AI for Portuguese with Open Decoder Gervásio PT*](https://aclanthology.org/2024.sigul-1.3) (Santos et al., SIGUL-WS 2024)
ACL