
A novel approach for supervised clustering of text-based entity subsets that leverages open-source large language models, captures contextual information efficiently, and introduces an augmented triplet loss function to address the challenges of directly applying triplet loss to this problem.


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efficient-and-scalable-supervised-clustering-of-text-based-entities-using-large-language-models


Efficient and Scalable Supervised Clustering of Text-based Entities using Large Language Models
