DianJin/DianJin-CSC-Data
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--- license: mit --- <div align="center"> <img alt="image" src="https://raw.githubusercontent.com/aliyun/qwen-dianjin/refs/heads/master/images/dianjin_logo.png"> <p align="center"> <a href="https://tongyi.aliyun.com/dianjin">Qwen DianJin Platform</a> | <a href="https://github.com/aliyun/qwen-dianjin">Github</a> | <a href="https://modelscope.cn/organization/tongyi_dianjin">ModelScope</a> | <a href="https://arxiv.org/abs/2508.04423">Paper</a> </p> </div> ## 📢 Introduction<a name="summary"></a>  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 realworld 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 supporters 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 We open-source both the CSConv and RoleCS datasets to support research on customer support conversation systems. These resources are intended to facilitate model development, benchmarking, and further advances in the field. ## 🔖 Citation<a name="cite"></a> If you use our dataset, please cite our paper. ``` @article{dianjin-csc, title = {Evaluating, Synthesizing, and Enhancing for Customer Support Conversation}, author = {Jie Zhu, Huaixia Dou, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang, and Fang Kong}, journal = {arxiv}, year = {2025} } ```
CSConv is an evaluation dataset of real-world customer-agent conversations rewritten using large language models to reflect deliberate strategy use, and it is annotated accordingly. RoleCS is a role-playing training dataset that uses large language models to generate strategy-rich conversations aligned with the CSC framework. These two datasets are intended to support the development of customer support conversation systems, benchmarking, and further research in the field.
