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arXiv · 2509.19593

GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models

Dylan Hutson·Daniel Vennemeyer·Aneesh Deshmukh·Justin Zhan·Tianyu Jiang
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Abstract

We introduce GuessingGame, a protocol for evaluating large language models (LLMs) as strategic question-askers in open-ended, open-domain settings. A Guesser LLM identifies a hidden object by posing free-form questions to an Oracle without predefined choices or candidate lists. To measure question quality, we propose two information gain (IG) metrics: a Bayesian method that tracks belief updates over semantic concepts using LLM-scored relevance, and an entropy-based method that filters candidates via ConceptNet. Both metrics are model-agnostic and support post hoc analysis. Across 858 games with multiple models and prompting strategies, higher IG strongly predicts efficiency: a one-standard-deviation IG increase reduces expected game length by 43\%. Prompting constraints guided by IG, such as enforcing question diversity, enable weaker models to significantly improve performance. These results show that question-asking in LLMs is both measurable and improvable, and crucial for interactive reasoning.

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Dylan Hutson, Daniel Vennemeyer, Aneesh Deshmukh, Justin Zhan, Tianyu Jiang. 2025-09-23. GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models. https://arxiv.org/abs/2509.19593

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