arXiv:2602.14279v2 Announce Type: replace-cross Abstract: Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocating limited questioning effort under real costs and missing data. Although large language models enable adaptive, multi-turn interactions in natural language, most existing elicitation methods optimize what to ask with a fixed respondent pool, and do not adapt respondent selection or leverage population structure when responses are partial or incomplete. To address this gap, we study adaptive grou
The rapid advancement of large language models makes adaptive, multi-turn interactions feasible, enabling more sophisticated information elicitation methods.
Improving the efficiency and accuracy of information elicitation from diverse groups can significantly impact decision-making in various fields, from policy to market research.
Traditional static survey methods are being replaced by adaptive, AI-driven approaches that dynamically select respondents and tailor questions based on partial responses and population structures.
- · AI agents developers
- · Market research firms
- · Policy makers
- · Organizations requiring collective intelligence
- · Traditional survey companies
- · Static questionnaire designers
More accurate and efficient data collection from groups becomes possible through adaptive AI.
Decision-making processes across industries improve due to richer, dynamically sourced collective insights.
The development of highly specialized AI agents for information gathering could automate significant portions of research and analysis workflows.
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