Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions

ArXi:2602.14279v2 Announce Type: replace 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.