  Using large language models, this paper presents techniques to improve
extremely low-resourced indigenous language translations. Our approaches are
grounded in the use of (1) the presence of a datastore consisting of a limited
number of parallel translation examples, (2) the inherent capabilities of LLMs
like GPT-3.5, and (3) a word-level translation dictionary. We harness the
potential of LLMs and in-context learning techniques in such a setting for
using LLMs as universal translators for extremely low-resourced languages. Our
methodology hinges on utilizing LLMs as language compilers for selected
language pairs, hypothesizing that they could internalize syntactic structures
to facilitate accurate translation. We introduce three techniques: KNNPrompting
with Retrieved Prompting Context, Chain-of-Thought Prompting and
Learningfrom-Mistakes Prompting, with the last method addressing past errors.
The evaluation results suggest that, even with limited corpora, LLMs can
effectively translate extremely low-resource languages when paired with proper
prompting.
