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Found 15 papers, 4 papers with code
Forking Paths in Neural Text Generation
Estimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users.
The Illusion-Illusion: Vision Language Models See Illusions Where There are None
Here, I invert the standard use of perceptual illusions to examine basic processing errors in current vision language models.
Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models
We asked human respondents (N=178 in total) to evaluate images generated by a state-of-the-art image-generating AI (DALL-E 3) prompted with these `logical probes', and find that none reliably produce human agreement scores greater than 50\%.
One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity
Researchers in social science and psychology have recently proposed using large language models (LLMs) as replacements for humans in behavioral research.
MMToM-QA: Multimodal Theory of Mind Question Answering
To engineer multimodal ToM capacity, we propose a novel method, BIP-ALM (Bayesian Inverse Planning Accelerated by Language Models).
Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks
Intuitive psychology is a pillar of common-sense reasoning.
Type theory in human-like learning and inference
Humans can generate reasonable answers to novel queries (Schulz, 2012): if I asked you what kind of food you want to eat for lunch, you would respond with a food, not a time.
Testing Relational Understanding in Text-Guided Image Generation
Relations are basic building blocks of human cognition.
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy.
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics
As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.
Unsupervised Discovery of 3D Physical Objects
We study the problem of unsupervised physical object discovery.
Temporal and Object Quantification Nets
We aim to learn generalizable representations for complex activities by quantifying over both entities and time, as in “the kicker is behind all the other players,” or “the player controls the ball until it moves toward the goal.” Such a structural inductive bias of object relations, object quantification, and temporal orders will enable the learned representation to generalize to situations with varying numbers of agents, objects, and time courses.
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from developmental psychology.
A Compositional Object-Based Approach to Learning Physical Dynamics
By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.
Help or Hinder: Bayesian Models of Social Goal Inference
Everyday social interactions are heavily influenced by our snap judgments about others goals.
