用大模型生成虚拟客户画像，让客服机器人更懂用户需求。
PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG
- 用少样本和思维链提示生成虚拟客户画像
- 知识库加入画像后，回答准确率从5.88升至6.42
- 81.8%用户认为更新后的系统对业务有帮助
大型语言模型（LLMs）的出现显著提升了自然语言处理中对客户画像的分析能力。在沃尔沃建筑设备公司（VCE），传统客户画像依赖定性方法，耗时且难以扩展。本文旨在生成合成客户画像，并将其集成到检索增强生成（RAG）聊天机器人中，以支持商业决策。首先，构建基于验证画像的RAG聊天机器人；其次，采用少样本和思维链（CoT）提示技术生成合成画像，并通过麦纳马拉检验评估其完整性、相关性和一致性；最后，将合成画像与额外细分信息加入聊天机器人知识库，评估响应准确率与实际应用价值。结果显示，少样本提示生成的画像更完整，而思维链提示在响应时间和令牌消耗上更高效。知识库更新后，聊天机器人平均准确率从10分制的5.88提升至6.42，81.82%参与者认为新系统在业务场景中具有实用价值。
原文摘要 · Abstract (English)
The introduction of Large Language Models (LLMs) has significantly transformed Natural Language Processing (NLP) applications by enabling more advanced analysis of customer personas. At Volvo Construction Equipment (VCE), customer personas have traditionally been developed through qualitative methods, which are time-consuming and lack scalability. The main objective of this paper is to generate synthetic customer personas and integrate them into a Retrieval-Augmented Generation (RAG) chatbot to support decision-making in business processes. To this end, we first focus on developing a persona-based RAG chatbot integrated with verified personas. Next, synthetic personas are generated using Few-Shot and Chain-of-Thought (CoT) prompting techniques and evaluated based on completeness, relevance, and consistency using McNemar's test. In the final step, the chatbot's knowledge base is augmented with synthetic personas and additional segment information to assess improvements in response accuracy and practical utility. Key findings indicate that Few-Shot prompting outperformed CoT in generating more complete personas, while CoT demonstrated greater efficiency in terms of response time and token usage. After augmenting the knowledge base, the average accuracy rating of the chatbot increased from 5.88 to 6.42 on a 10-point scale, and 81.82% of participants found the updated system useful in business contexts.
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