Do Large Language Models Know What Humans Know?

Sean Trott, Cameron Robert Jones, James Michaelov, Tyler A. Chang, Ben Bergen

Abstract:

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from
an innate biological endowment or from experience accrued through child development, particularly
exposure to language describing others’ mental states. We test the viability of the language exposure
hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre-registered analyses, we
present a linguistic version of the False Belief Task to both human participants and a large language
model, GPT-3. Both are sensitive to others’ beliefs, but while the language model significantly exceeds
chance behavior, it does not perform as well as the humans nor does it explain the full extent of their
behavior—despite being exposed to more language than a human would in a lifetime. This suggests
that while statistical learning from language exposure may in part explain how humans develop the
ability to reason about the mental states of others, other mechanisms are also responsible.