  To what extent can experience from language contribute to our conceptual
knowledge? Computational explorations of this question have shed light on the
ability of powerful neural language models (LMs) -- informed solely through
text input -- to encode and elicit information about concepts and properties.
To extend this line of research, we present a framework that uses
neural-network language models (LMs) to perform property induction -- a task in
which humans generalize novel property knowledge (has sesamoid bones) from one
or more concepts (robins) to others (sparrows, canaries). Patterns of property
induction observed in humans have shed considerable light on the nature and
organization of human conceptual knowledge. Inspired by this insight, we use
our framework to explore the property inductions of LMs, and find that they
show an inductive preference to generalize novel properties on the basis of
category membership, suggesting the presence of a taxonomic bias in their
representations.
