Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 28 Apr 2024.
Builds on
16 of 29 references.
References · 16
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4 more
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMscited by 26
- 2025Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languagescited by 3
- 2026Challenges and Recommendations for LLM-as-a-Judge in Multilingual Settings and for Low-Resource Languagescited by 1
- 2025Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languagescited by 0
- 2026Challenges and Recommendations for LLM-as-a-Judge in Multilingual Settings and for Low-Resource Languages2 Jul 2026
- 2025Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages23 Jun 2025
- 2025Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs20 Feb 2025
- 2025Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages14 Feb 2025