7 papers
Towards aligned body representations in vision models
Andrey Gizdov, Andrea Procopio, Yichen Li +2
Human physical reasoning relies on internal "body" representations - coarse, volumetric approximations that capture an object's extent and support intuitive predictions about motio…
Chain of Time: In-Context Physical Simulation with Image Generation Models
YingQiao Wang, Eric Bigelow, Boyi Li +1
We propose a novel cognitively-inspired method to improve and interpret physical simulation in vision-language models. Our ``Chain of Time" method involves generating a series of i…
Re-evaluating Theory of Mind evaluation in large language models
Jennifer Hu, Felix Sosa, Tomer Ullman
The question of whether large language models (LLMs) possess Theory of Mind (ToM) -- often defined as the ability to reason about others' mental states -- has sparked significant s…
Shades of Zero: Distinguishing Impossibility from Inconceivability
Jennifer Hu, Felix Sosa, Tomer Ullman
Some things are impossible, but some things may be even more impossible than impossible. Levitating a feather using one's mind is impossible in our world, but fits into our intuiti…
Forking Paths in Neural Text Generation
Eric Bigelow, Ari Holtzman, Hidenori Tanaka +1
Estimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users. However, prior approaches to uncertainty estimation…
Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models
Colin Conwell, Rupert Tawiah-Quashie, Tomer Ullman
Despite remarkable progress in multi-modal AI research, there is a salient domain in which modern AI continues to lag considerably behind even human children: the reliable deployme…