\section{Conclusion}
\label{sec:conclusion}
We describe our systems for the 2024 AmericasNLP Shared Task on the Creation of Educational Materials for Indigenous Languages, which include LLM-based systems, character-level neural networks, and finetuned multilingual models. We observe potential benefits from augmentation strategies for character-level models, particularly the \textit{sentence copying} strategy, which helps a model adapt to new examples.

However, we find that nearly all of our systems, with the exception of the LLM system, do not generalize well to the covered test set, resulting in poor performance on the shared task. These results reaffirm the difficulty of training robust neural models in low-resource scenarios and the importance of thorough validation.