Papers by Justin Zhan
GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models excel at factual recall, arithmetic reasoning, multi-turn dialogue . their capacity as askers, formulating strategic, adaptive, and information-seeking questions, remains less explored . |
| Approach: | They propose a protocol for evaluating large language models as strategic question-askers . they propose entropy-based methods that filter candidates via ConceptNet and Bayesian method that tracks belief updates over semantic concepts . |
| Outcome: | The proposed method is model-agnostic and supports post hoc analysis. |