7 papers
On the use of foundation models in cognitive science
Raj Sanjay Shah, Alex Warstadt, Michael Frank +1
A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations of their correspondence to ad…
Human-Like Anaphor Resolution in Large Language Models
Keane Zhang, Varshini Chinta, Raj Sanjay Shah +1
Anaphors are expressions that refer to other expressions, called antecedents. The process of connecting the two is called resolution. Cognitive science has identified multiple fact…
When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations
Harsh Nishant Lalai, Raj Sanjay Shah, Hanspeter Pfister +2
Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models…
The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilities
Harsh Nishant Lalai, Raj Sanjay Shah, Jiaxin Pei +3
Large Language Models (LLMs) have been extensively tuned to mitigate explicit biases, yet they often exhibit subtle implicit biases rooted in their pre-training data. Rather than d…
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning
James P Jun, Vijay Marupudi, Raj Sanjay Shah +1
Learning new information without forgetting prior knowledge is central to human intelligence. In contrast, neural network models suffer from catastrophic forgetting: a significant…
Modeling Understanding of Story-Based Analogies Using Large Language Models
Kalit Inani, Keshav Kabra, Vijay Marupudi +1
Recent advancements in Large Language Models (LLMs) have brought them closer to matching human cognition across a variety of tasks. How well do these models align with human perfor…