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

David Ifeoluwa Adelani, A. Seza Doğruöz, André Coneglian, Atul Kr. Ojha
4/28/2024
cs.CL

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Abstract

Large Language Models are transforming NLP for a variety of tasks. However, how LLMs perform NLP tasks for low-resource languages (LRLs) is less explored. In line with the goals of the AmericasNLP workshop, we focus on 12 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 part of speech (POS) labeling of LRLs in comparison to HRLs. We explain the reasons behind this failure and provide an error analysis through examples observed in our data set.

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Cite this paper

@article{adelani2024comparing,
  title  = {Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages},
  author = {David Ifeoluwa Adelani and A. Seza Doğruöz and André Coneglian and Atul Kr. Ojha},
  year   = {2024},
  eprint = {2404.18286},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.18286v2}
}

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