一句话：提出基于大语言模型的在线工单升级框架 TickIt，实现主题感知、动态更新与关系驱动的工单分派，并在火山引擎落地验证。
在大规模云服务系统中，工单是解决客户问题、维持服务质量的关键机制，但传统人工升级流程效率低、准确性差，难以应对海量且复杂的工单；已有机器学习分类方法又忽视了动态工单更新、按主题路由以及工单关系分析等实际升级需求。本文提出 TickIt，一个由大语言模型驱动的在线工单升级框架：它持续更新工单状态，将工单分配给最合适的支持团队，挖掘工单之间的关联，并借助类别引导的有监督微调不断改进性能。作者将 TickIt 部署在字节跳动的云服务平台火山引擎上，验证了其有效性与实用性，为大规模云服务系统的自动化工单升级提供了重要进展。
In large-scale cloud service systems, support tickets serve as a critical mechanism for resolving customer issues and maintaining service quality. However, traditional manual ticket escalation processes encounter significant challenges, including inefficiency, inaccuracy, and difficulty in handling the high volume and complexity of tickets. While previous research has proposed various machine learning models for ticket classification, these approaches often overlook the practical demands of real-world escalations, such as dynamic ticket updates, topic-specific routing, and the analysis of ticket relationships. To bridge this gap, this paper introduces TickIt, an innovative online ticket escalation framework powered by Large Language Models. TickIt enables topic-aware, dynamic, and relationship-driven ticket escalations by continuously updating ticket states, assigning tickets to the most appropriate support teams, exploring ticket correlations, and leveraging category-guided supervised fine-tuning to continuously improve its performance. By deploying TickIt in ByteDance's cloud service platform Volcano Engine, we validate its efficacy and practicality, marking a significant advancement in the field of automated ticket escalation for large-scale cloud service systems.
@inproceedings{paperbot9040,
title = {TickIt: Leveraging Large Language Models for Automated Ticket Escalation},
author = {Fengrui Liu and Xiao He and Tieying Zhang and Jianjun Chen and Yi Li and Lihua Yi and Haipeng Zhang and Gang Wu and Rui Shi},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728558}
}