C hat M ap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025)

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.

## introduction with the widespread application of large language models (llms), research has increasingly focused on developing domain-specific conversational agents @xcite @xcite , especially customer service chatbots for e-commerce platforms (sánchez cuadrado et al., 2024; @xcite . to achieve coherent, human-like conversations with customers, llms need to align their dialogue strategies with human behavior. most approaches employ supervised fine-tuning (sft) or retrieval-augmented generation (rag) on large-scale human dialogue datasets @xcite @xcite ). yet, these methods face dual challenges: (1) models implicitly acquire dialogue patterns, making it difficult to explicitly grasp core dialogue rules. (2) human dialogue datasets inherently contain substantial noise, such as pleasantries, emotional statements, and offtopic discussions, which obstruct effective information extraction and learning @xcite . alternative methods inspired by human cognition propose first extracting dialogue patterns from corpora and then incorporating them into the model's learning @xcite . however, (1) existing dialogue strategy induction work heavily relies on manual effort, requiring human reading of large amounts of dialogue, followed by classification and annotation of dialogue strategies, which is labor-intensive and difficult to generalize @xcite . (2) while recent llm-based automation attempts address this bottleneck @xcite , they predominantly focus on surface-level behavior abstraction, such as dialogue intentions (e.g., appeasing the customer) and strategies used (e.g., questioning the customer to understand their needs), while neglecting the learning of how to apply logical reasoning in different contexts to select appropriate dialogue strategies @xcite . in other words, they focus only on "what was done" without capturing "why it was done," which is crucial for understanding the rationale behind strategy selection. to address these challenges, we propose chatmap, a multi-agent system that automatically mines human customer service agents' thought processes in dialogues to enhance chatbot performance. based on the raw dialogue dataset collected from interactions between human agents and customers on taobao, a popular e-commerce platform in china, chatmap follows a structured mining process comprising the following steps: (1) extract requests and solutions from historical conversations. (2) cluster similar requests. (3) induce higher-level service thoughts from the requests and solutions within each cluster. (4) post-process by retaining representative service thoughts to reduce their number. (5) inspect the quality of service thoughts and introduce a reflection mechanism for regeneration. ultimately, we obtain a refined and reasonable dataset of service thoughts. during utilization, the customer service chatbot generates appropriate responses based on the dialogue history, the customer's current request, and the corresponding service thought. the overview of our chatmap framework is illustrated in figure 1 . the main contributions of our work can be summarized as follows: ## method figure 2 illustrates the pipeline of chatmap. in this section, we detail chatmap including the source and structure of our dialogue dataset ( §3.1), the methods for mining thought processes and refining dialogue extraction dialogue abstraction service thought induction post-processing quality inspection service thought utilization raw dialogue dataset service thought hello. this cup has a problem. alright, let me check… round 1: i have submitted a refund request, thank you. you're welcome! have a great day, see you! round m: topic name：refund request ## experiment we are aiming to investigate the following research questions: (1) can chatmap automatically mine thought processes to achieve human-level results and improve chatbot performance offline and online? ( §4.2) (2) is our multi-agent design effective? ( §4.3) (3) does our evaluation method, chateval-ec, show greater agreement with human evaluations? ( §4.4.1) (4) what is the impact of different module orders? ( §4.4.2) (5) how does the number of service thoughts affect performance? ( §4.4.3) ## conclusion in this paper, we introduce chatmap, a novel multiagent approach that automatically induces customer service dialogue strategies. by progressively processing raw dialogues through extraction, abstraction, induction, post-processing, and quality inspection, it obtains service thoughts to help chatbots generate more appropriate responses. extensive experiments validate the effectiveness of our approach in improving offline metrics and confirm the reliability of our chateval-ec evaluation design. by mining the thought process, chatmap outperforms other methods, showcasing capabilities comparable to professional human agents. additionally, online a/b experiments have substantiated improved business performance.