用大模型自动提取客服通话关键信息，降本增效。
LLM-Based Insight Extraction for Contact Center Analytics and Cost-Efficient Deployment
- 构建大模型系统自动生成通话主题标签
- 实测不同模型组合可降低70%部署成本
- 适合需要智能分析海量客服录音的团队
大语言模型已改变客服中心行业，在提升自助服务、简化行政流程和增强坐席效率方面表现突出。本文提出一个自动化通话驱动生成系统，为话题建模、来电分类、趋势检测和常见问题生成提供基础，向客服人员和管理者输出可操作的洞察。我们设计了一种成本高效的LLM系统架构，包含：1）对自研、开源及微调模型的全面评估；2）低成本部署策略；3）在生产环境中部署时的对应成本分析。
原文摘要 · Abstract (English)
Large Language Models have transformed the Contact Center industry, manifesting in enhanced self-service tools, streamlined administrative processes, and augmented agent productivity. This paper delineates our system that automates call driver generation, which serves as the foundation for tasks such as topic modeling, incoming call classification, trend detection, and FAQ generation, delivering actionable insights for contact center agents and administrators to consume. We present a cost-efficient LLM system design, with 1) a comprehensive evaluation of proprietary, open-weight, and fine-tuned models and 2) cost-efficient strategies, and 3) the corresponding cost analysis when deployed in production environments.
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