AbstractTelecommunications operators are tasked with enhancing service quality, reducing operational costs, and preserving customer privacy. This study presents an innovative application of large language models (LLMs) integrated with the LangChain technology framework, aimed at revolutionizing customer service in the telecom sector. The LangChain framework features a Knowledge Organizing Module and a Knowledge Search Module, both designed to refine customer support operations. The research develops an LLM‐based approach to improve the segmentation and organization of knowledge bases, tailored for the telecommunications industry. This approach ensures seamless integration with existing LLMs while preserving distinct knowledge domains, crucial for search accuracy. Additionally, the framework includes an advanced information security protocol with a robust filtering system that effectively excludes sensitive data from the model's outputs, enhancing data security. Empirical findings indicate that the ChatGLM2‐6B+LangChain model outperforms the baseline ChatGLM2, demonstrating heightened effectiveness in telecom‐specific tasks and outstripping even more sophisticated models like GPT‐4. The implementation of this LLM‐based framework within telecom customer service systems has significantly sharpened the precision of knowledge recommendations, as reflected by a dramatic increase in acceptance rates from 15% to 70%.

The technology development trend of intelligent customer service for operators was discussed, and automatic speech recognition (ASR) transcription error correction technology, semantic extraction technology, encrypted database, and agent access control technology were introduced.Two different research directions of error detection and automatic error correction for speech recognition were analyzed, two directions of supervised learning and unsupervised learning were discussed, and big data encryption and agent access control technologies were introduced.At the same time, the technical directions of multimodal interaction technology, intelligent recommendation technology and disabled-oriented services were prospected.In conclusion, the development and innovation of intelligent customer service technology will bring a more efficient and convenient service experience to the communication industry and strongly promote the progress of the industry service level.

Automatic speech recognition (ASR) has been widely used in the field of customer service, but the performance of general ASR in dialect transcription is not satisfactory, especially in Guangdong Province. Targeted training of ASR transcription engine will produce effect, but the training cost is high, and it is not suitable for small-scale training with multiple dialects and frequencies. The complaint problems in the customer service field have obvious clustering and are suitable for few-shot and multi-frequency training. In view of this, in the actual engineering application, the method of ASR transcribed into the dialect error correction thesaurus is tried to be used to replace the wrong words, and have achieved good results. The optimization technology after automatic speech transcription proposed in this study can improve the recognition accuracy of general ASR by 13.75% for dialect words.

The evolution of customer service platforms was reviewed systematically, covering the first generation of interactive voice response (IVR) customer service, the second generation of multimedia online customer service, and the third generation of artificial intelligence (AI) customer service.The functional characteristics of each generation of customer service platforms were elaborated in depth.Additionally, the functional construction of customer service systems in various industries was introduced in detail, such as manufacturing, finance, transportation, and telecommunications.Based on this, the application components of AI customer service platforms for operators were further depicted, including intelligent dispatching, voice transcription, knowledge recommendation, intelligent quality inspection, and intelligent liability determination.Currently, the adoption of AI customer service platforms by operators has become a development trend.Looking forward, customer service platforms will integrate various intelligent technologies such as large-scale models, natural language processing (NLP), and knowledge graphs to meet customer service demands.

随着运营商产品的日益丰富和客户对服务质量期望的日益提高,如何充分调动企业内部各业务部门全力协同提升服务质量,一直困扰着电信运营商.针对这一问题,利用智能化客服系统,将运营商服务质量总目标分解成若干子目标,形成多个管控点;并对应到企业内各部门,制定举措及计划;同时通过检查组做好闭环检查对标;重构了运营商OPC(Object-Plan-Check)客服管理体系.在此基础上,通过广州电信的实际案例进行了验证,取的了明显的成效.