Comparing LLM Prompting With Cross-lingual Transfer Performance On Indigenous And Low-resource Brazilian Languages
Adelani David Ifeoluwa, Doğruöz A. Seza, Coneglian André, Ojha Atul Kr.. Arxiv 2024
[Paper]
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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