2504.04204: Adaptive Elicitation of Latent Information Using Natural Language

This research paper introduces a novel framework for adaptive information elicitation using natural language, addressing the challenge of understanding latent entities that cannot be directly observed. This framework employs meta-learned language models to predict future observations and quantify uncertainty, enabling the strategic selection of the most informative questions to reduce this uncertainty. The authors propose an approach that learns from historical question-answer data to effectively gather information about new, unseen entities in domains like student assessment and opinion polling. By focusing on a predictive view of uncertainty, their method avoids the complexities of directly modeling latent variables, showcasing improved performance over baseline methods in identifying unknowns and enhancing downstream predictions. The paper also contributes a new dataset for the "Twenty Questions" game to facilitate further research in this area.