让聊天系统在自然对话中高效获取用户信息
Proactive User Information Acquisition via Chats on User-Favored Topics
- 通过预设话题的对话主动获取用户信息
- 大模型在该任务上成功率仍较低
- 适合需隐私信息的智能助手场景
以提供实际价值（如分享新闻、预防老年人衰弱）为目标的对话系统，常需通过与用户偏好话题的聊天来主动获取特定信息。本研究提出PIVOT任务，旨在推进此类系统的技术基础。该任务要求系统在预设话题的对话中，自然地获取用户对预定义问题的回答，且不令用户感到突兀。研究发现，即使近期的大语言模型在该任务中表现仍不佳。为此，我们构建了一个适用于分析的专用数据集，并基于该数据集的洞察，开发出一种简单但有效的解决方案。
原文摘要 · Abstract (English)
Chat-oriented dialogue systems designed to provide tangible benefits, such as sharing the latest news or preventing frailty in senior citizens, often require Proactive acquisition of specific user Information via chats on user-faVOred Topics (PIVOT). This study proposes the PIVOT task, designed to advance the technical foundation for these systems. In this task, a system needs to acquire the answers of a user to predefined questions without making the user feel abrupt while engaging in a chat on a predefined topic. We found that even recent large language models (LLMs) show a low success rate in the PIVOT task. We constructed a dataset suitable for the analysis to develop more effective systems. Finally, we developed a simple but effective system for this task by incorporating insights obtained through the analysis of this dataset.
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