@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://aclanthology.org/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."
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%0 Conference Proceedings
%T LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations
%A Pattnayak, Priyaranjan
%A Chowdhuri, Sanchari
%A Agarwal, Amit
%A Patel, Hitesh Laxmichand
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S 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
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F pattnayak-etal-2025-llm
%X 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.
%U https://aclanthology.org/2025.ijcnlp-long.170/
%P 3180-3206
Markdown (Informal)
[LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations](https://aclanthology.org/2025.ijcnlp-long.170/) (Pattnayak et al., IJCNLP-AACL 2025)
ACL
- Priyaranjan Pattnayak, Sanchari Chowdhuri, Amit Agarwal, and Hitesh Laxmichand Patel. 2025. LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations. In 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, pages 3180–3206, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.