Design of a large language model for improving customer service in telecom operators

Telecommunications 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%. This study addresses the inefficiencies of traditional customer service systems in telecom operations, which need help with timely data retrieval and precision. A customized large language model (LLM) is developed using the LangChain framework tailored for telecom customer service. The model's performance is further enhanced through reinforcement learning, significantly reducing the dissemination of incorrect information. The experimental findings demonstrate a substantial increase in the acceptance rate of the model's recommendations, from 15% to 70%, indicating its efficacy and reliability in environments with limited resources. image