NBQ: Next-Best-Question for Dynamic Profiling
Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person. We study the Next-Best-Question (NBQ) problem: at each turn, an interviewer should ask the question with the highest expected information gain given what has already been learned and the conversation goal. We propose NBQ, a plug-and-play framework that seeds a diverse pool of candidate questions, maintains a compact and continuously updated user state, adaptively selects the next question within a turn budget, and distills the resulting free-form dialogue into a structured vector-based user profile. As a demanding applic...
| Authors | Yimin Shi, Clarice Wang, Haixun Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 May 2026 |
| Last updated at source | 2 June 2026 |
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