Large Language Models Enable Few-Shot Clustering
Vijay Viswanathan Kiril Gashteovski Carolin Lawrence Tongshuang Wu Graham Neubig
Unlike traditional unsupervised clustering, semi-supervised clustering allows
users to provide meaningful structure to the data, which helps the clustering
algorithm to match the user's intent. Existing approaches to semi-supervised
clustering require a significant amount of feedback from an expert to improve
the clusters. In this paper, we ask whether a large language model can amplify
an expert's guidance to enable query-efficient, few-shot semi-supervised text
clustering. We show that LLMs are surprisingly effective at improving
clustering. We explore three stages where LLMs can be incorporated into
clustering: before clustering (improving input features), during clustering (by
providing constraints to the clusterer), and after clustering (using LLMs
post-correction). We find incorporating LLMs in the first two stages can
routinely provide significant improvements in cluster quality, and that LLMs
enable a user to make trade-offs between cost and accuracy to produce desired
clusters. We release our code and LLM prompts for the public to use.