<ul data-eligibleForWebStory="false"><li>The paper discusses the importance of eliciting information to reduce uncertainty about latent entities in various domains using natural language.</li><li>Current large language models and fine-tuning algorithms lack mechanisms for strategically gathering information to refine their understanding of latent entities.</li><li>A proposed adaptive elicitation framework utilizes a meta-learned language model to actively reduce uncertainty by simulating future observations and quantifying uncertainty in natural language.</li><li>Experiments on games and assessments show that the framework outperforms baselines in identifying critical unknowns and improving predictions, showcasing the potential of strategic information gathering in natural language contexts.</li></ul>

Adaptive Elicitation of Latent Information Using Natural Language

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