4 papers
Reasoning Large Language Model Errors Arise from Hallucinating Critical Problem Features
Alex Heyman, Joel Zylberberg
Large language models have recently made great strides in reasoning task performance through chain-of-thought (CoT) strategies trained via reinforcement learning; however, these "r…
Evaluating the Systematic Reasoning Abilities of Large Language Models through Graph Coloring
Alex Heyman, Joel Zylberberg
Contemporary large language models are powerful problem-solving tools, but they exhibit weaknesses in their reasoning abilities which ongoing research seeks to mitigate. We investi…
How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?
Greta Tuckute, Dawn Finzi, Eshed Margalit +8
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no cons…
Finding Shared Decodable Concepts and their Negations in the Brain
Cory Efird, Alex Murphy, Joel Zylberberg +1
Prior work has offered evidence for functional localization in the brain; different anatomical regions preferentially activate for certain types of visual input. For example, the f…