Get the gist? Using large language models for few-shot decontextualization

Benjamin Kane; Lenhart Schubert

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arXiv · 2310.06254

Get the gist? Using large language models for few-shot decontextualization

Benjamin Kane·Lenhart Schubert

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Abstract

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.

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Benjamin Kane, Lenhart Schubert. 2023-10-10. Get the gist? Using large language models for few-shot decontextualization. https://arxiv.org/abs/2310.06254

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