Multi-Modal Training System for Customer Service Representatives Using Large Language Models

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup>.

Authors

Institutions

Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/cait64506.2024.10962811
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Modal Training System for Customer Service Representatives Using Large Language Models

Xin Xie, Long Fan, Xiaoli Yang, Rongyu Cui
Technology and Data Analysis
article

Multi-Modal Training System for Customer Service Representatives Using Large Language Models

Xin Xie, Long Fan, Xiaoli Yang, Rongyu Cui
article en

Abstract

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup>.

Chengdu University (CN), Neusoft (China) (CN), Chengdu Neusoft University
Openalex Percentile: Top 53%
Technology and Data Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.