Related papers: Large Language Models Enable Few-Shot Clustering

Large Language Models Enable Few-Shot Clustering

URL: http://arxiv.org/abs/2307.00524v1
Date: Sun, 2 Jul 2023 09:17:11 GMT
Title: Large Language Models Enable Few-Shot Clustering
Authors: Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, Tongshuang Wu, Graham Neubig
Abstract summary: We show that large language models can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We find incorporating LLMs in the first two stages can routinely provide significant improvements in cluster quality.
Score: 88.06276828752553
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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