Evaluating, Synthesizing, and Enhancing for Customer Support
  Conversation
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.
6895590348b0ae5ca2710d1b
Jie Zhu
claimed_verified
2025-08-08T16:20:02.912Z
642656cbad1e3b0e6e91b752
/avatars/3bf0ee15fd528e09b2b889f5cce3cbd0.svg
Jie Zhu
user
amazingj
6895590348b0ae5ca2710d1c
Huaixia Dou
6895590348b0ae5ca2710d1d
Junhui Li
6895590348b0ae5ca2710d1e
Lifan Guo
6895590348b0ae5ca2710d1f
Feng Chen
6895590348b0ae5ca2710d20
Chi Zhang
6895590348b0ae5ca2710d21
Fang Kong
Evaluating, Synthesizing, and Enhancing for Customer Support
  Conversation
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.
A structured framework and datasets for training customer service agents using well-defined support strategies improve the quality of customer support interactions and problem resolution.
COPC guidelines conversational stages support strategies CSConv LLMs role-playing approach RoleCS fine-tuning high-quality responses