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Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages

Publication type
C1
Publication status
Published
Authors
Adelani, D., Doğruöz, A., Coneglian, A., & Ojha, A.
Editor
Manuel Mager, Abteen Ebrahimi, Shruti Rijhwani, Arturo Oncevay, Luis Chiruzzo, Robert Pugh and Katharina von der Wense
Series
Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)
Pagination
34-41
Publisher
Association for Computational Linguistics (ACL)
Conference
4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024) (Mexico City, New Mexico)
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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.