Multi-Modal Training System for Customer Service Representatives Using Large Language Models
Xin Yu Xie, Rongyu Cui, Long Fan, Xiaoli Yang · 2024
This paper introduces an advanced multi-modal training system aimed at enhancing the performance of customer service representatives (CSRs) in managing various customer complaints. This system incorporates large language model (LLM) technology, text-to-speech (TTS) technology and speech-to-text conversion. This combination enables realistic simulations of both logical and illogical customer complaints, crucial for thorough CSRs training. Furthermore, the system features an intelligent coaching agent that assesses trainee responses using advanced customer service theories, offering immediate feedback and actionable insights. When tested in a large call center, the system showed substantial improvements in trainee performance, customer satisfaction, and the resolution rate of customer complaints. These results highlight the potential of multi-modal LLM-based training systems to transform training methodologies in the customer service sector[1].