arXiv:2606.00809v1 Announce Type: new Abstract: 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 nex
The proliferation of advanced AI models and the increasing demand for efficient knowledge discovery in conversational settings are driving the development of sophisticated profiling and interaction frameworks.
This development addresses a core challenge in human-AI interaction by proposing a systematic approach to dynamic profiling, which can significantly enhance the utility of AI in various real-world applications.
The ability of AI to conduct more targeted, efficient, and goal-driven conversations for information gathering will improve, leading to better outcomes in areas like hiring, customer service, and content creation.
- · AI software developers
- · Recruitment platforms
- · Customer service industries
- · Content creators
More efficient and personalized AI-driven conversational interfaces will emerge across various sectors.
Reduced need for human intervention in initial screening and information gathering processes, potentially automating aspects of white-collar work.
Ethical considerations around data privacy, bias, and manipulation in AI-driven profiling will become more prominent, requiring robust regulatory frameworks.
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Read at arXiv cs.AI