Context-Sensitive Semantic Reasoning in Large Language Models

Tyler Giallanza, Declan Iain Campbell

Track: short paper (up to 5 pages)

Keywords: context, attention, large language models, semantic reasoning, semantic cognition, semantic knowledge

Abstract:

The development of large language models (LLMs) holds promise for increasing
the scale and breadth of experiments probing human cognition. LLMs will be
useful for studying the human mind to the extent that their behaviors and their
representations are aligned with humans. Here we test this alignment by mea-
suring the degree to which LLMs reproduce the context-sensitivity demonstrated
by humans in semantic reasoning tasks. We show in two simulations that, like
humans, the behavior of leading LLMs is sensitive to both local context and task
context, reasoning about the same item differently when it is presented in different
contexts or tasks. However, the representations derived from LLM text embedding
models do not exhibit the same degree of context sensitivity. These results suggest
that LLMs may provide useful models of context-dependent human behavior, but
cognitive scientists should be cautious when assuming that embeddings reflect the
same context sensitivity.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 70