@inproceedings{pattnayak-etal-2025-llm,
    title = "{LLM}-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations",
    author = "Pattnayak, Priyaranjan  and
      Chowdhuri, Sanchari  and
      Agarwal, Amit  and
      Patel, Hitesh Laxmichand",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.170/",
    pages = "3180--3206",
    ISBN = "979-8-89176-298-5",
    abstract = "Clustering customer chat data is vital for cloud providers handling multi-service queries. Traditional methods struggle with overlapping concerns and create broad, static clusters that degrade over time. Re-clustering disrupts continuity, making issue tracking difficult. We propose an adaptive system that segments multi-turn chats into service-specific concerns and incrementally refines clusters as new issues arise. Cluster quality is tracked via Davies{--}Bouldin Index (DBI) and Silhouette Scores, with LLM-based splitting applied only to degraded clusters. Our method improves Silhouette Scores by over 100{\%} and reduces DBI by 65.6{\%} compared to baselines, enabling scalable, real-time analytics without full re-clustering."
}