2 citations · 6 across the 17 of their papers we have counts for
7 papers · 1 filter
Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models
Grace Guo, Jenna Jiayi Kang, Raj Sanjay Shah +2
Vision Language Models (VLMs) have been successful at many chart comprehension tasks that require attending to both the images of charts and their accompanying textual descriptions…
Development of Cognitive Intelligence in Pre-trained Language Models
Raj Sanjay Shah, Khushi Bhardwaj, Sashank Varma
Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). The increasing cognitive alignment of these models has made them candidat…
How Well Do Deep Learning Models Capture Human Concepts? The Case of the Typicality Effect
Siddhartha K. Vemuri, Raj Sanjay Shah, Sashank Varma
How well do representations learned by ML models align with those of humans? Here, we consider concept representations learned by deep learning models and evaluate whether they sho…
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention
Andrew Li, Xianle Feng, Siddhant Narang +4
When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in ps…
Towards a Path Dependent Account of Category Fluency
David Heineman, Reba Koenen, Sashank Varma
Category fluency is a widely studied cognitive phenomenon, yet two conflicting accounts have been proposed as the underlying retrieval mechanism -- an optimal foraging process deli…
Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning
Xin Lian, Sashank Varma, Christopher J. MacLellan
Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the…
Natural Mitigation of Catastrophic Interference: Continual Learning in Power-Law Learning Environments
Atith Gandhi, Raj Sanjay Shah, Vijay Marupudi +1
Neural networks often suffer from catastrophic interference (CI): performance on previously learned tasks drops off significantly when learning a new task. This contrasts strongly…