4 papers · 1 filter
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 \citep{jones2026llms, zeng2026lvlms} reach apparently contradictory conclusions about whether large vision-language models (LVLMs) can coordinate similarly to hu…
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…