@inproceedings{adelani-etal-2024-comparing,
title = "Comparing {LLM} prompting with Cross-lingual transfer performance on Indigenous and Low-resource {B}razilian Languages",
author = {Adelani, David Ifeoluwa and
Do{\u{g}}ru{\"o}z, A. Seza and
Coneglian, Andr{\'e} and
Ojha, Atul Kr.},
editor = "Mager, Manuel and
Ebrahimi, Abteen and
Rijhwani, Shruti and
Oncevay, Arturo and
Chiruzzo, Luis and
Pugh, Robert and
von der Wense, Katharina",
booktitle = "Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.americasnlp-1.5",
doi = "10.18653/v1/2024.americasnlp-1.5",
pages = "34--41",
abstract = "Large Language Models are transforming NLP for a lot of tasks. However, how LLMs perform NLP tasks for LRLs is less explored. In alliance with the theme track of the NAACL{'}24, we focus on 12 low-resource languages (LRLs) from Brazil, 2 LRLs from Africa and 2 high-resource languages (HRLs) (e.g., English and Brazilian Portuguese). Our results indicate that the LLMs perform worse for the labeling of LRLs in comparison to HRLs in general. We explain the reasons behind this failure and provide an error analyses through examples from 2 Brazilian LRLs.",
}
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<abstract>Large Language Models are transforming NLP for a lot of tasks. However, how LLMs perform NLP tasks for LRLs is less explored. In alliance with the theme track of the NAACL’24, we focus on 12 low-resource languages (LRLs) from Brazil, 2 LRLs from Africa and 2 high-resource languages (HRLs) (e.g., English and Brazilian Portuguese). Our results indicate that the LLMs perform worse for the labeling of LRLs in comparison to HRLs in general. We explain the reasons behind this failure and provide an error analyses through examples from 2 Brazilian LRLs.</abstract>
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%0 Conference Proceedings
%T Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages
%A Adelani, David Ifeoluwa
%A Doğruöz, A. Seza
%A Coneglian, André
%A Ojha, Atul Kr.
%Y Mager, Manuel
%Y Ebrahimi, Abteen
%Y Rijhwani, Shruti
%Y Oncevay, Arturo
%Y Chiruzzo, Luis
%Y Pugh, Robert
%Y von der Wense, Katharina
%S Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F adelani-etal-2024-comparing
%X Large Language Models are transforming NLP for a lot of tasks. However, how LLMs perform NLP tasks for LRLs is less explored. In alliance with the theme track of the NAACL’24, we focus on 12 low-resource languages (LRLs) from Brazil, 2 LRLs from Africa and 2 high-resource languages (HRLs) (e.g., English and Brazilian Portuguese). Our results indicate that the LLMs perform worse for the labeling of LRLs in comparison to HRLs in general. We explain the reasons behind this failure and provide an error analyses through examples from 2 Brazilian LRLs.
%R 10.18653/v1/2024.americasnlp-1.5
%U https://aclanthology.org/2024.americasnlp-1.5
%U https://doi.org/10.18653/v1/2024.americasnlp-1.5
%P 34-41
Markdown (Informal)
[Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages](https://aclanthology.org/2024.americasnlp-1.5) (Adelani et al., AmericasNLP-WS 2024)
ACL