Cognitive Effects in Large Language Models
This work addresses potential biases in LLMs for users and researchers, but it is incremental as it applies existing methods to a new model.
The study tested GPT-3 on human cognitive effects, finding it prone to priming, distance, SNARC, and size congruity effects but not anchoring, with results indicating systematic patterns similar to humans.
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.