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Liability Judgment Method of Telecom Customer Complaint Based on Linked-RAG and Large Language Models
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Zhengren LI1, Jiabao HUANG1, Yunxin TAO2, Tingjie LU1, Fei CHEN1
Journal of Beijing University of Posts and Telecommunications | 2025, 48(5) : 25 - 31
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Journal of Beijing University of Posts and Telecommunications | 2025, 48(5): 25-31
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Liability Judgment Method of Telecom Customer Complaint Based on Linked-RAG and Large Language Models
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Zhengren LI1, Jiabao HUANG1, Yunxin TAO2, Tingjie LU1, Fei CHEN1
Affiliations
  • 1.School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 2.School of Sports Engineering, Beijing Sport University, Beijing 100084, China
doi: 10.13190/j.jbupt.2025-062
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With the exponential growth of telecommunications business scale, customer complaint responsibility determination has become a key link in the compliance management of communication operators. Traditional methods face challenges such as semantic decoupling difficulties and low efficiency of knowledge reuse when dealing with complex complaints and unstructured historical cases. This study, based on the existing business process and large language model technology, proposes a linked retrieval enhanced generation framework. Through hierarchical semantic decoupling and dynamic retrieval of historical knowledge, it achieves the precision and efficiency of complaint responsibility determination. The framework proposes a two-level complaint point splitting mechanism:The first level uses a hybrid semantic parsing to strip off compound demands, and the second level extracts core complaint points from the first-level complaint list through large model prompt technology, while dynamically processing historical documents to adapt to the second-level complaint matching, completing the structural transformation of the database. Finally, experiments based on 423 actual complaint data from a provincial operator show that compared with several traditional retrieval augmented generation(RAG)models,this method improves the retrieval accuracy by 18.87% and the retrieval recall rate by 14.13% . This research provides an efficient and reusable technical path for intelligent responsibility determination under a strong regulatory background and has important practical significance.

retrieval augmented generation  /  complaint judgment  /  semantic decoupling  /  knowledge integration  /  large language model
Zhengren LI, Jiabao HUANG, Yunxin TAO, Tingjie LU, Fei CHEN. Liability Judgment Method of Telecom Customer Complaint Based on Linked-RAG and Large Language Models[J]. Journal of Beijing University of Posts and Telecommunications, 2025 , 48 (5) : 25 -31 . DOI: 10.13190/j.jbupt.2025-062
Year 2025 volume 48 Issue 5
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doi: 10.13190/j.jbupt.2025-062
  • Receive Date:2025-06-21
  • Online Date:2026-04-16
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  • Received:2025-06-21
Affiliations
    1.School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2.School of Sports Engineering, Beijing Sport University, Beijing 100084, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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