Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages
CoRR(2024)
摘要
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 AmeicasNLP 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 analyses through examples
observed in our data set.
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