Portraying Large Language Models as Machines, Tools, or Companions Affects What Mental Capacities Humans Attribute to Them

As large language models (LLMs) become increasingly popular and prevalent in media and daily conversations, individuals encounter different portrayals of LLMs from various sources. It is important to understand how these portrayals can shape their beliefs about LLMs as this can have downstream impacts on adoption and usage behaviors. In this work, we investigate what mental capacities individuals attribute to LLMs after being exposed to short videos adopting one of three portrayals: mechanistic (LLMs as machines), functional (LLMs as tools), and intentional (LLMs as companions). We find that the intentional portrayal increases the attribution of mental capacities to LLMs, and that individuals tend to attribute mind-related capacities the most, followed by heart-then body-related capacities. We discuss the implications of these findings, provide recommendations on how to portray LLMs, and outline directions for future research.