---
title: DianJin-CSC-Data
canonical_url: "https://www.modelscope.cn/datasets/tongyi_dianjin/DianJin-CSC-Data"
md_url: "https://www.modelscope.cn/datasets/tongyi_dianjin/DianJin-CSC-Data.md"
repository: tongyi_dianjin/DianJin-CSC-Data
chinese_name: DianJin-CSC-Data
last_updated: 2025-12-04
license: mit
storage_size: "72 MB"
downloads: 767
stars: 2
---

# DianJin-CSC-Data

> DianJin-CSC-Data - tongyi_dianjin 在 ModelScope 开源的数据集。Qwen DianJin Platform | Github | ModelScope | Paper

tongyi_dianjin/DianJin-CSC-Data 是 ModelScope 魔搭社区上的数据集，存储大小 72 MB，采用 mit 许可。

- **Repository**: tongyi_dianjin/DianJin-CSC-Data
- **License**: mit
- **Storage size**: 72 MB
- **Downloads**: 767
- **Stars**: 2
- **Last updated**: 2025-12-04

Source: https://www.modelscope.cn/datasets/tongyi_dianjin/DianJin-CSC-Data

---

<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>

![](./images/example.png)

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}
}
```