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New framework optimizes LLM-driven group elicitation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for adaptive group elicitation, which optimizes the selection of both questions and respondents in multi-turn interactions. This method uses a heterogeneous graph neural network to aggregate responses and participant attributes, enabling imputation of missing data and guiding respondent selection. The approach aims to improve population-level response prediction under budget constraints, showing significant gains on real-world opinion datasets. AI

IMPACT This framework could improve the efficiency of data collection in surveys and collective assessments by optimizing LLM interactions.

RANK_REASON The cluster contains a research paper detailing a new framework for adaptive group elicitation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework optimizes LLM-driven group elicitation

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The cluster contains a research paper detailing a new framework for adaptive group elicitation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ruomeng Ding, Tianwei Gao, Thomas P. Zollo, Eitan Bachmat, Richard Zemel, Zhun Deng ·

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

    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…

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