  Interlinear glossed text (IGT) is a popular format in language documentation
projects, where each morpheme is labeled with a descriptive annotation.
Automating the creation of interlinear glossed text can be desirable to reduce
annotator effort and maintain consistency across annotated corpora. Prior
research has explored a number of statistical and neural methods for
automatically producing IGT.
  As large language models (LLMs) have showed promising results across
multilingual tasks, even for rare, endangered languages, it is natural to
wonder whether they can be utilized for the task of generating IGT. We explore
whether LLMs can be effective at the task of interlinear glossing with
in-context learning, without any traditional training. We propose new
approaches for selecting examples to provide in-context, observing that
targeted selection can significantly improve performance. We find that
LLM-based methods beat standard transformer baselines, despite requiring no
training at all. These approaches still underperform state-of-the-art
supervised systems for the task, but are highly practical for researchers
outside of the NLP community, requiring minimal effort to use.
