用大模型动态拆分客服对话，实现持续精准聚类。
LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations
- 基于大模型识别对话中的服务诉求，动态拆分与更新聚类。
- 聚类质量提升：轮廓系数提高100%，戴维斯-鲍尔丁指数降低65.6%。
- 适合需要实时分析、避免频繁重聚类的客服系统场景。
处理多服务查询的云服务商亟需对客户聊天数据进行聚类分析。传统方法难以应对重叠诉求，生成宽泛且静态的聚类，随时间推移质量下降；重新聚类又破坏问题追踪连续性。本文提出一种自适应系统，将多轮对话分解为具体服务诉求，并在新问题出现时增量式优化聚类。通过戴维斯-鲍尔丁指数（DBI）和轮廓系数（Silhouette Scores）监控聚类质量，仅对退化聚类应用大模型驱动的分裂策略。相比基线方法，该方法使轮廓系数提升超过100%，DBI降低65.6%，支持无需全量重聚类的可扩展实时分析。
原文摘要 · Abstract (English)
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. Reclustering 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 DaviesBouldin Index 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 reclustering.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。