A Large-Language-Model–Driven End-to-End Intelligent Framework for Responsibility Determination in Telecom Complaints

ZHANG Mengting; ZHANG Xiaohang; LI Zhengren; WANG Haiyan; CHEN Zhonghua

doi:10.11959/j.issn.1000-0801.DXKX250712

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A Large-Language-Model–Driven End-to-End Intelligent Framework for Responsibility Determination in Telecom Complaints

更新时间：2026-05-11

- A Large-Language-Model–Driven End-to-End Intelligent Framework for Responsibility Determination in Telecom Complaints
- Telecommunications Science (2026)
- 作者机构：
  
  1.北京邮电大学北京 100876
  2.中国信息通信研究院北京 100876
  3.中国移动通信集团内蒙古有限公司内蒙古呼和浩特 010090
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(72271034);The National Key R&D Program Xiongan Special Project(2023XAGG009304)
- DOI：10.11959/j.issn.1000-0801.DXKX250712
  CLC： TP18;F626
- Received：11 December 2025，
  
  Revised：2026-03-25，
  
  Accepted：11 May 2026，
- 稿件说明：
移动端阅览
ZHANG Mengting, ZHANG Xiaohang, LI Zhengren, et al. A Large-Language-Model–Driven End-to-End Intelligent Framework for Responsibility Determination in Telecom Complaints[J/OL]. Telecommunications Science, 2026.
DOI：

ZHANG Mengting, ZHANG Xiaohang, LI Zhengren, et al. A Large-Language-Model–Driven End-to-End Intelligent Framework for Responsibility Determination in Telecom Complaints[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX250712.

摘要

电信申诉处理涉及多个环节，传统人工模式在效率与一致性上存在瓶颈，现有自动化研究多局限于单点任务。基于此提出一种基于大语言模型的智能化判责框架，通过提示词工程依次构建申诉无效判断、类型识别、内容拆解、人机协同证据链收集与判责报告生成五个模块。在某省级运营商约7000条真实工单上的实验表明：无效判断准确率达83.2%，较BERT等基线方法最低提升14.2%；服务顽疾分类准确率为97%；申诉内容拆解F1值为73.9%，要点覆盖率为82.1%；各环节处理效率较人工提升最高达96.7%。跨月份泛化测试显示模型性能稳定，为电信申诉判责提供了完整的智能化解决方案，对行业智能化实践具有参考价值。

Abstract

Telecom complaint adjudication involves multiple stages. Traditional manual processing faces clear bottlenecks in efficiency and consistency

while existing automation studies mostly focus on isolated tasks. To address this issue

this paper proposes an intelligent adjudication framework based on large language models. Through prompt engineering

the framework comprises five modules: invalid complaint detection

complaint type identification

complaint content decomposition

human-in-the-loop evidence chain collection

and adjudication report generation. Experiments on approximately 7

000 real complaint cases from a provincial telecom operator show that the proposed method achieves 83.2% accuracy in invalid complaint detection

outperforming BERT and other baselines by at least 14.2%; 97% accuracy in service issue classification; and an F1 score of 73.9% with 82.1% key-point coverage in complaint content decomposition. Processing efficiency across stages improves by up to 96.7% compared with manual handling. Cross-month generalization tests further demonstrate stable performance. The proposed framework provides a complete intelligent solution for telecom complaint adjudication and offers practical value for digital transformation in the telecom industry.

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references

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