Get the gist? Using large language models for few-shot decontextualization
This addresses the need for efficient decontextualization in NLP systems like information retrieval or dialogue, reducing reliance on expensive annotations, though it is incremental as it builds on existing generative models.
The paper tackled the problem of decontextualizing sentences for reuse in NLP applications by proposing a few-shot method using large language models, achieving viable performance across multiple domains with only a small set of examples.
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.