Cognitive Effects in Large Language Models
Jonathan Shaki Sarit Kraus Michael Wooldridge
Large Language Models (LLMs) such as ChatGPT have received enormous attention
over the past year and are now used by hundreds of millions of people every
day. The rapid adoption of this technology naturally raises questions about the
possible biases such models might exhibit. In this work, we tested one of these
models (GPT-3) on a range of cognitive effects, which are systematic patterns
that are usually found in human cognitive tasks. We found that LLMs are indeed
prone to several human cognitive effects. Specifically, we show that the
priming, distance, SNARC, and size congruity effects were presented with GPT-3,
while the anchoring effect is absent. We describe our methodology, and
specifically the way we converted real-world experiments to text-based
experiments. Finally, we speculate on the possible reasons why GPT-3 exhibits
these effects and discuss whether they are imitated or reinvented.