# DianJin-CSC-Data

**Repository Path**: thinker0/DianJin-CSC-Data

## Basic Information

- **Project Name**: DianJin-CSC-Data
- **Description**: CSConv是一个基于真实世界客户与客服人员对话的评价数据集，通过大型语言模型重写，以体现明确的策略使用，并进行了相应的标注。RoleCS是一个角色扮演的训练数据集，使用大型语言模型生成，与CSC框架对齐的富有策略性的对话。这两个数据集旨在支持客户支持对话系统的模型开发、基准测试和进一步研究。
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-11
- **Last Updated**: 2025-12-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

---
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>

![](./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}
}
```