  My doctoral research focuses on understanding semantic knowledge in neural
network models trained solely to predict natural language (referred to as
language models, or LMs), by drawing on insights from the study of concepts and
categories grounded in cognitive science. I propose a framework inspired by
'inductive reasoning,' a phenomenon that sheds light on how humans utilize
background knowledge to make inductive leaps and generalize from new pieces of
information about concepts and their properties. Drawing from experiments that
study inductive reasoning, I propose to analyze semantic inductive
generalization in LMs using phenomena observed in human-induction literature,
investigate inductive behavior on tasks such as implicit reasoning and emergent
feature recognition, and analyze and relate induction dynamics to the learned
conceptual representation space.
