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

arXiv: https://arxiv.org/abs/2404.18286 · HTML: https://mlsanity.com/paper/2404.18286/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (2023)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Composable Sparse Fine-Tuning for Cross-Lingual Transfer](https://mlsanity.com/paper/2110.07560/) (2021)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://mlsanity.com/paper/2010.12858/) (2020)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks](https://mlsanity.com/paper/2311.07463/) (2023)
- [How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://mlsanity.com/paper/2106.02124/) (2021)
- [Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://mlsanity.com/paper/2010.03179/) (2020)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [Specializing Multilingual Language Models: An Empirical Study](https://mlsanity.com/paper/2106.09063/) (2021)
- [On Building Spoken Language Understanding Systems for Low Resourced Languages](https://mlsanity.com/paper/2205.12818/) (2022)

## Built on by (most cited first, 4 of 4)

- [Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs](https://mlsanity.com/paper/2502.14830/) (2025)
- [Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages](https://mlsanity.com/paper/2502.10140/) (2025)
- [Challenges and Recommendations for LLM-as-a-Judge in Multilingual Settings and for Low-Resource Languages](https://mlsanity.com/paper/2607.02235/) (2026)
- [Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages](https://mlsanity.com/paper/2506.19187/) (2025)

## 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.