  In many NLP applications that involve interpreting sentences within a rich
context -- for instance, information retrieval systems or dialogue systems --
it is desirable to be able to preserve the sentence in a form that can be
readily understood without context, for later reuse -- a process known as
``decontextualization''. While previous work demonstrated that generative
Seq2Seq models could effectively perform decontextualization after being
fine-tuned on a specific dataset, this approach requires expensive human
annotations and may not transfer to other domains. We propose a few-shot method
of decontextualization using a large language model, and present preliminary
results showing that this method achieves viable performance on multiple
domains using only a small set of examples.
