ProxyLLM : LLM-Driven Framework for Customer Support Through Text-Style Transfer

TL;DR

This paper introduces ProxyLLM, a framework that uses large language models to transform customer messages' tone, reducing emotional stress on agents and improving overall customer support experiences.

Contribution

It presents a novel LLM-driven system that modifies customer message tone to alleviate agent stress, implemented as an easily integrable Chrome extension.

Findings

Reduces emotional exhaustion in customer service agents

Improves agent efficiency and customer satisfaction

Easily integrates into existing support systems

Abstract

Chatbot-based customer support services have significantly advanced with the introduction of large language models (LLMs), enabling enhanced response quality and broader application across industries. However, while these advancements focus on reducing business costs and improving customer satisfaction, limited attention has been given to the experiences of customer service agents, who are critical to the service ecosystem. A major challenge faced by agents is the stress caused by unnecessary emotional exhaustion from harmful texts, which not only impairs their efficiency but also negatively affects customer satisfaction and business outcomes. In this work, we propose an LLM-powered system designed to enhance the working conditions of customer service agents by addressing emotionally intensive communications. Our proposed system leverages LLMs to transform the tone of customer messages, preserving actionable content while mitigating the emotional impact on human agents. Furthermore, the application is implemented as a Chrome extension, making it highly adaptable and easy to integrate into existing systems. Our method aims to enhance the overall service experience for businesses, customers, and agents.