Users do not trust recommendations from a large language model more than AI-sourced snippets - McGrath, Melanie J.; Cooper, Patrick S.; Duenser, Andreas (Frontiers in Computer Science, 2024)
Background: The ability of large language models to generate general purpose natural language represents a significant step forward in creating systems able to augment a range of human endeavors. However, concerns have been raised about the potential for misplaced trust in the potentially hallucinatory outputs of these models.The study reported in this paper is a preliminary exploration of whether trust in the content of output generated by an LLM may be inflated in relation to other forms of ecologically valid, AI-sourced information.Method: Participants were presented with a series of general knowledge questions and a recommended answer from an AI-assistant that had either been generated by an ChatGPT-3 or sourced by Google's AI-powered featured snippets function. We also systematically varied whether the AI-assistant's advice was accurate or inaccurate.Results: Trust and reliance in LLM-generated recommendations were not significantly higher than that of recommendations from a non-LLM source. While accuracy of the recommendations resulted in a significant reduction in trust, this did not differ significantly by AI-application.Using three predefined general knowledge tasks and fixed recommendation sets from the AI-assistant, we did not find evidence that trust in LLM-generated output is artificially inflated, or that people are more likely to miscalibrate their trust in this novel technology than another commonly drawn on form of AI-sourced information.