While the top performers demonstrate a remarkable ability to recognize fictional taxonomy, the models at the opposite end of the spectrum reveal interesting patterns in knowledge assertion behavior. Some models consistently claim intermediate or extensive knowledge about nonexistent species, suggesting they may be generating plausible-sounding information rather than acknowledging uncertainty.
This disparity in performance highlights the importance of careful model selection for scientific applications. Models that readily fabricate details about fictional organisms may also be prone to hallucinating information about real but lesser-known species, potentially introducing errors into scientific workflows that depend on accurate microbial information.