51 citations · 213 across the 35 of their papers we have counts for
6 papers · 1 filter
Where did the ambiguity go? Examining how multimodal models interpret polysemous words
Jasin Cekinmez, Addison J. Wu, Raja Marjieh +1
Human language is highly polysemous. Many common words (e.g., "bank" or "palm") carry several distinct meanings that shape what humans communicate and imagine. Large language model…
Visual serial processing deficits explain divergences in human and VLM reasoning
Nicholas Budny, Kia Ghods, Declan Campbell +6
Why do Vision Language Models (VLMs), despite success on standard benchmarks, often fail to match human performance on surprisingly simple visual reasoning tasks? While the underly…
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
Sreejan Kumar, Raja Marjieh, Byron Zhang +5
Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game…
Improving Interpersonal Communication by Simulating Audiences with Language Models
Ryan Liu, Howard Yen, Raja Marjieh +2
How do we communicate with others to achieve our goals? We use our prior experience or advice from others, or construct a candidate utterance by predicting how it will be received.…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw +2
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Ag…