无需人工编排的客服自动化框架，兼顾隐私与流程引导。
Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework
- 用任务导向流程图抽象服务对话，实现端到端自动执行
- 本地部署小模型，通过去中心化蒸馏解决数据少和隐私问题
- 支持快速构建客服系统，适合需要隐私保护的场景
客服自动化在数字化转型中需求日益增长。现有方法或依赖复杂的模块化系统编排，或采用过于简化的指令模板，缺乏有效引导且泛化能力差。本文提出一种无编排框架，利用任务导向流程图（TOFs）实现无需人工干预的端到端自动化。首先定义了TOF的组成要素与评估指标，进而提出一种成本高效的流程图构建算法，从服务对话中抽象程序性知识。强调小型语言模型的本地部署，并提出基于流程图的去中心化蒸馏方法，以缓解训练中的数据稀缺与隐私问题。大量实验验证了该框架在多种服务任务中的有效性，性能优于强基线及市场产品。通过发布基于网页的系统演示与案例研究，旨在推动未来客服自动化的高效构建。
原文摘要 · Abstract (English)
Customer service automation has seen growing demand within digital transformation. Existing approaches either rely on modular system designs with extensive agent orchestration or employ over-simplified instruction schemas, providing limited guidance and poor generalizability. This paper introduces an orchestration-free framework using Task-Oriented Flowcharts (TOFs) to enable end-to-end automation without manual intervention. We first define the components and evaluation metrics for TOFs, then formalize a cost-efficient flowchart construction algorithm to abstract procedural knowledge from service dialogues. We emphasize local deployment of small language models and propose decentralized distillation with flowcharts to mitigate data scarcity and privacy issues in model training. Extensive experiments validate the effectiveness in various service tasks, with superior quantitative and application performance compared to strong baselines and market products. By releasing a web-based system demonstration with case studies, we aim to promote streamlined creation of future service automation.
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