This work investigates the in-context learning abilities of pretrained large
language models (LLMs) when instructed to translate text from a low-resource
language into a high-resource language as part of an automated machine
translation pipeline. We conduct a set of experiments translating Southern
Quechua to Spanish and examine the informativity of various types of
information retrieved from a constrained database of digitized pedagogical
materials (dictionaries and grammar lessons) and parallel corpora. Using both
automatic and human evaluation of model output, we conduct ablation studies
that manipulate (1) context type (morpheme translations, grammar descriptions,
and corpus examples), (2) retrieval methods (automated vs. manual), and (3)
model type. Our results suggest that even relatively small LLMs are capable of
utilizing prompt context for zero-shot low-resource translation when provided a
minimally sufficient amount of relevant linguistic information. However, the
variable effects of prompt type, retrieval method, model type, and
language-specific factors highlight the limitations of using even the best LLMs
as translation systems for the majority of the world's 7,000+ languages and
their speakers.

本研究通过一系列实验翻译南安第斯克丘亚语为西班牙语，探究预训练大型语言模型在自动化机器翻译中从低资源语言到高资源语言的上下文学习能力。实验结果表明，即使是相对较小的语言模型，在提供了足够相关语言信息的情况下，能够通过提示上下文实现零样本低资源翻译。然而，提示类型、检索方法、模型类型和语言特定因素的不确定效果突显了即使是最佳的语言模型也存在于世界上 7000 多种语言及其使用者的翻译系统中的限制。