@inproceedings{jiang-etal-2025-chatmap,
title = "{C}hat{M}ap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration",
author = "Jiang, Xinyi and
Hu, Tianyi and
Qin, Yuheng and
Wang, Guoming and
Huan, Zhou and
Chen, Kehan and
Huang, Gang and
Lu, Rongxing and
Tang, Siliang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.617/",
doi = "10.18653/v1/2025.findings-acl.617",
pages = "11927--11947",
ISBN = "979-8-89176-256-5",
abstract = "Leveraging Large Language Models (LLMs) to build domain-specific conversational agents, especially for e-commerce customer service chatbots, is a growing focus. While existing methods enhance dialogue performance by extracting core patterns from dialogue data and integrating them into models, two key challenges persist: (1) heavy reliance on human experts for dialogue strategy induction, and (2) LLM-based automatic extraction often focuses on summarizing specific behaviors, neglecting the underlying thought processes behind strategy selection. In this paper, we present ChatMap, which focuses on enhancing customer service chatbots by mining thought processes using a Multi-Agent aPproach. Specifically, the process begins by extracting customer requests and solutions from a raw dialogue dataset, followed by clustering similar requests, analyzing the thought processes behind solutions, and refining service thoughts. Through a quality inspection and reflection mechanism, the final service thought dataset is generated, helping chatbots provide more appropriate responses. Offline experimental results show that ChatMap performs comparably to manually annotated thought processes and significantly outperforms other baselines, demonstrating its ability to automate human annotation and enhance dialogue capabilities through strategic understanding. Online A/B tests on Taobao, a popular e-commerce platform in China reveal that ChatMap can better improve customer satisfaction and address customer requests from a business perspective."
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<abstract>Leveraging Large Language Models (LLMs) to build domain-specific conversational agents, especially for e-commerce customer service chatbots, is a growing focus. While existing methods enhance dialogue performance by extracting core patterns from dialogue data and integrating them into models, two key challenges persist: (1) heavy reliance on human experts for dialogue strategy induction, and (2) LLM-based automatic extraction often focuses on summarizing specific behaviors, neglecting the underlying thought processes behind strategy selection. In this paper, we present ChatMap, which focuses on enhancing customer service chatbots by mining thought processes using a Multi-Agent aPproach. Specifically, the process begins by extracting customer requests and solutions from a raw dialogue dataset, followed by clustering similar requests, analyzing the thought processes behind solutions, and refining service thoughts. Through a quality inspection and reflection mechanism, the final service thought dataset is generated, helping chatbots provide more appropriate responses. Offline experimental results show that ChatMap performs comparably to manually annotated thought processes and significantly outperforms other baselines, demonstrating its ability to automate human annotation and enhance dialogue capabilities through strategic understanding. Online A/B tests on Taobao, a popular e-commerce platform in China reveal that ChatMap can better improve customer satisfaction and address customer requests from a business perspective.</abstract>
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%0 Conference Proceedings
%T ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration
%A Jiang, Xinyi
%A Hu, Tianyi
%A Qin, Yuheng
%A Wang, Guoming
%A Huan, Zhou
%A Chen, Kehan
%A Huang, Gang
%A Lu, Rongxing
%A Tang, Siliang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F jiang-etal-2025-chatmap
%X Leveraging Large Language Models (LLMs) to build domain-specific conversational agents, especially for e-commerce customer service chatbots, is a growing focus. While existing methods enhance dialogue performance by extracting core patterns from dialogue data and integrating them into models, two key challenges persist: (1) heavy reliance on human experts for dialogue strategy induction, and (2) LLM-based automatic extraction often focuses on summarizing specific behaviors, neglecting the underlying thought processes behind strategy selection. In this paper, we present ChatMap, which focuses on enhancing customer service chatbots by mining thought processes using a Multi-Agent aPproach. Specifically, the process begins by extracting customer requests and solutions from a raw dialogue dataset, followed by clustering similar requests, analyzing the thought processes behind solutions, and refining service thoughts. Through a quality inspection and reflection mechanism, the final service thought dataset is generated, helping chatbots provide more appropriate responses. Offline experimental results show that ChatMap performs comparably to manually annotated thought processes and significantly outperforms other baselines, demonstrating its ability to automate human annotation and enhance dialogue capabilities through strategic understanding. Online A/B tests on Taobao, a popular e-commerce platform in China reveal that ChatMap can better improve customer satisfaction and address customer requests from a business perspective.
%R 10.18653/v1/2025.findings-acl.617
%U https://aclanthology.org/2025.findings-acl.617/
%U https://doi.org/10.18653/v1/2025.findings-acl.617
%P 11927-11947
Markdown (Informal)
[ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration](https://aclanthology.org/2025.findings-acl.617/) (Jiang et al., Findings 2025)
ACL
- Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, and Siliang Tang. 2025. ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11927–11947, Vienna, Austria. Association for Computational Linguistics.