Context-Aware Clustering using Large Language Models

arXiv:2405.00988v1 Announce Type: new
Abstract: Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed that powerful closed-source LLMs provide good quality clusterings of entity sets but are not scalable due to the massive compute power required and the associated costs. Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and effective supervised clustering of entity …

abstract arxiv clustering compute context cs.cl cs.lg good language language models large language large language models llms massive power quality scalable success tasks text text understanding type understanding