让客服聊天机器人主动挖取有价值信息，同时减少用户负担。
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation
- 设计主动探查任务，优化提问时机以减少对话轮次。
- 提出PROCHATIP框架，能精准把握提问节奏，信息获取效率更高。
- 适合希望提升客服数据价值的企业与研究者。
客服聊天机器人正被期待超越被动响应，成为获取高价值信息与商业智能的战略接口。为此，本文提出三项贡献：1）定义新型主动信息探查任务，旨在优化预设目标信息的提问时机，同时最小化对话轮次与用户摩擦；2）提出PROCHATIP框架，包含专门训练的对话策略模块，掌握提问时机的微妙平衡；3）实验表明，PROCHATIP显著优于基线模型，在信息获取与服务质量方面表现更优。我们认为，本工作有效重塑了聊天机器人的商业价值，使其成为可扩展、低成本的主动商业智能引擎。代码已开源：https://github.com/SCUNLP/PROCHATIP。
原文摘要 · Abstract (English)
Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting high-value information and business intelligence. In response, we make three main contributions. 1) We introduce and define a novel task of Proactive Information Probing, which optimizes when to probe users for pre-specified target information while minimizing conversation turns and user friction. 2) We propose PROCHATIP, a proactive chatbot framework featuring a specialized conversation strategy module trained to master the delicate timing of probes. 3) Experiments demonstrate that PROCHATIP significantly outperforms baselines, exhibiting superior capability in both information probing and service quality. We believe that our work effectively redefines the commercial utility of chatbots, positioning them as scalable, cost-effective engines for proactive business intelligence. Our code is available at https://github.com/SCUNLP/PROCHATIP.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。