4 papers
Inverse Turing Bench: Evaluating Language Models as Judges of Human vs. AI Dialogue
William Hager, Ishika Rathi, Masum Hasan +1
As AI systems integrate into online spaces, differentiating them from humans in conversations is increasingly important. We present Inverse Turing Bench, a benchmark that evaluates…
Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication
Peter Zeng, Amie J. Paige, Weiling Li +3
Two recent studies (Jones et al. (2026); Zeng et al. (2026)) reach apparently contradictory conclusions about whether LVLMs can coordinate on efficient referring expressions. We co…
Large Language Models Persuade Without Planning Theory of Mind
Jared Moore, Rasmus Overmark, Ned Cooper +3
A growing body of work attempts to evaluate the theory of mind (ToM) abilities of humans and large language models (LLMs) using static, non-interactive question-and-answer benchmar…
Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task
Jared Moore, Ned Cooper, Rasmus Overmark +3
Recent evidence suggests Large Language Models (LLMs) display Theory of Mind (ToM) abilities. Most ToM experiments place participants in a spectatorial role, wherein they predict a…