用大模型生成相似问题，提升客服机器人合规性与满意度。
Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models
- 基于上下文生成语义相近的问答对，增强知识库覆盖
- 部署后用户满意度达92%，较基线提升18%
- 适合需合规、防幻觉的客服系统优化
基于检索的客服聊天机器人依赖人工验证的问答知识，确保响应准确且符合监管要求。为应对多样化的客户咨询，通过引入语义一致但表达多样的“相似问题”来扩充知识库是一种低成本有效策略。本文提出针对大语言模型的相似问题生成（SQG）任务，设计上下文感知方法以实现全面语义探索并增强与源问答对的对齐。提出优化技术用于构建上下文提示，并在预算约束下选择最优相似问题子集进行知识扩展。定量与人工评估均验证了方法有效性，在实际部署的聊天机器人中实现92%的用户满意度，相较未扩充基线提升18%。结果表明SQG具有实用价值，凸显大模型作为非生成式系统支持工具的潜力，适用于无幻觉、合规保障的应用场景。
原文摘要 · Abstract (English)
Retrieval-based chatbots leverage human-verified Q\&A knowledge to deliver accurate, verifiable responses, making them ideal for customer-centric applications where compliance with regulatory and operational standards is critical. To effectively handle diverse customer inquiries, augmenting the knowledge base with "similar questions" that retain semantic meaning while incorporating varied expressions is a cost-effective strategy. In this paper, we introduce the Similar Question Generation (SQG) task for LLM training and inference, proposing context-aware approaches to enable comprehensive semantic exploration and enhanced alignment with source question-answer relationships. We formulate optimization techniques for constructing in-context prompts and selecting an optimal subset of similar questions to expand chatbot knowledge under budget constraints. Both quantitative and human evaluations validate the effectiveness of these methods, achieving a 92% user satisfaction rate in a deployed chatbot system, reflecting an 18% improvement over the unaugmented baseline. These findings highlight the practical benefits of SQG and emphasize the potential of LLMs, not as direct chatbot interfaces, but in supporting non-generative systems for hallucination-free, compliance-guaranteed applications.
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