Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them

TL;DR

Public messages framing large language models as companions influence people's beliefs about their mental capacities and affect their reliance on LLMs for information, highlighting the power of discourse in shaping perceptions.

Contribution

This study demonstrates that framing LLMs as companions alters perceptions of their mental abilities and influences reliance on their responses, revealing discourse's role in shaping user beliefs.

Findings

Participants viewing 'companion' messages believed LLMs had more mental capacities.

Follow-up confirmed these effects and showed nuanced impacts on reliance for factual info.

Messages about LLMs shape user perceptions and reliance beyond technical features.

Abstract

How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we randomly assigned participants (N = 470) to watch short informational videos presenting LLMs as either machines, tools, or companions -- or to watch no video. We then assessed how strongly they believed LLMs to possess various mental capacities, such as the ability to have intentions or remember things. We found that participants who watched video messages presenting LLMs as companions reported believing that LLMs more fully possessed these capacities than did participants in other groups. In a follow-up study (N = 604), we replicated these findings and found nuanced effects on how these videos also impact people's reliance on LLM-generated responses when seeking out factual information. Together, these studies suggest that messages about LLMs -- beyond technical advances -- may shape what people believe about these systems and how they rely on LLM-generated responses.