In new research, Yan Leng, assistant professor of information, risk, and operations management at the McCombs School of Business at The University of Texas at Austin, has devised a sort of personality test — more precisely, a behavioral audit — for large language models (LLMs), the technology that drives products such as ChatGPT.

By understanding an LLM’s existing tendencies, an organization can decide whether an available model already fits its values and usage scenarios. If not, it might need to fine-tune a model before putting it to use.

Leng compares her framework to trying to understand a person through their actions and thought processes. “For a human, we would have our values, and our values would dictate how we make decisions, so we try to have that for LLMs as well,” she says.

Travelers who use chatbots powered by artificial intelligence on hotel booking platforms often feel uneasy. That discomfort can cause them to disengage or delay booking decisions, according to new research.

The study, from the Texas A&M College of Agriculture and Life Sciences and published in the International Journal of Hospitality Management, found consumers react most negatively to hospitality chatbots when the technology appears inaccurate or unreliable, deceptive or intrusive.

An artificial intelligence framework that suggests just one to three ingredient swaps can make meals meaningfully more nutritious and less expensive, according to a new study published in the open-access journal PLOS Digital Health by Trevor Chan and Ilias Tagkopoulos of the University of California, Davis, USA.

A new study, co-authored by a Penn State Smeal College of Business professor, shows that AI can mass-produce academic papers that look nearly indistinguishable from human-authored research, raising concerns about the potential impact on the academic community.