PersonaBOT：利用大语言模型与RAG将客户画像赋予生命
PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG
AI总结：
本文提出PersonaBOT，利用LLM和RAG生成合成客户画像并集成到聊天机器人中，通过少样本与思维链提示对比，提升业务决策支持，准确率评分从5.88升至6.42。
摘要：
大语言模型（LLMs）的引入显著改变了自然语言处理（NLP）应用，通过实现更先进的客户画像分析。在沃尔沃建筑设备公司（VCE），客户画像传统上通过定性方法开发，这些方法耗时且缺乏可扩展性。本文的主要目标是生成合成客户画像，并将其集成到基于检索增强生成（RAG）的聊天机器人中，以支持业务流程中的决策制定。为此，我们首先专注于开发一个基于画像的RAG聊天机器人，并集成经过验证的画像。接下来，使用少样本（Few-Shot）和思维链（CoT）提示技术生成合成画像，并使用McNemar检验基于完整性、相关性和一致性进行评估。在最后一步中，聊天机器人的知识库通过合成画像和额外的细分信息进行扩充，以评估响应准确性和实际效用的改进。关键发现表明，少样本提示在生成更完整的画像方面优于思维链，而思维链在响应时间和令牌使用方面表现出更高的效率。知识库扩充后，聊天机器人的平均准确率评分在10分制上从5.88提高到6.42，81.82%的参与者认为更新后的系统在业务环境中具有实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
