@inproceedings{pattnaik-etal-2024-improving,
    title = "Improving Hierarchical Text Clustering with {LLM}-guided Multi-view Cluster Representation",
    author = "Pattnaik, Anup  and
      George, Cijo  and
      Tripathi, Rishabh Kumar  and
      Vutla, Sasanka  and
      Vepa, Jithendra",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.emnlp-industry.54/",
    doi = "10.18653/v1/2024.emnlp-industry.54",
    pages = "719--727",
    abstract = "In this work, we present an approach that introduces different perspectives or views to improve the quality of hierarchical clustering of interaction drivers in a contact center. Specifically, we present a multi-stage approach that introduces LLM-guided multi-view cluster representation that significantly improves the quality of generated clusters. Our approach improves average Silhouette Score by upto 70{\%} and Human Preference Scores by 36.7{\%} for top-level clusters compared to standard agglomerative clustering for the given business use-case. We also present how the proposed approach can be adapted to cater to a standard non-hierarchical clustering use-cases where it achieves state-of-the-art performance on public datasets based on NMI and ACC scores, with minimal number of LLM queries compared to the current state-of-the-art approaches. Moreover, we apply our technique to generate two new labeled datasets for hierarchical clustering. We open-source these labeled datasets, validated and corrected by domain experts, for the benefit of the research community."
}