构建支持策略框架，用大模型生成高质量客服对话数据。
Evaluating, Synthesizing, and Enhancing for Customer Support Conversation
- 基于COPC指南定义五阶段十二策略，规范客服对话流程。
- 创建1855条策略标注的真实对话数据集CSConv和训练集RoleCS。
- LLM在RoleCS上微调后，在策略对齐与问题解决上显著提升。
高效客户服务不仅需要准确解决问题，还需结构化且富有同理心的沟通，符合专业标准。然而现有对话数据集缺乏战略指导，真实服务数据又难以获取与标注。为此，我们提出客户支持对话（CSC）任务，旨在训练客服人员使用明确的支持策略。我们基于COPC指南构建了结构化框架，定义了五个对话阶段和十二种策略以引导高质量交互。在此基础上，我们构建了包含1,855条真实客户-客服对话的评估数据集CSConv，通过大模型重写并标注策略使用情况；同时开发角色扮演方法，利用与CSC框架对齐的大模型角色生成策略丰富的对话，形成训练数据集RoleCS。实验表明，在RoleCS上微调强语言模型可显著提升其在CSConv上生成高质量、策略一致回复的能力。人工评估进一步证实其在问题解决率上的提升。所有代码与数据将公开于https://github.com/aliyun/qwen-dianjin。
原文摘要 · Abstract (English)
Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service agents to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer-agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution. All code and data will be made publicly available at https://github.com/aliyun/qwen-dianjin.
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