Generics in science communication: Misaligned interpretations across laypeople, scientists, and large language models
This addresses the problem of miscommunication in science for scientists, lay audiences, and AI developers, with incremental insights into interpretation mismatches.
The study investigated how generics in science communication are interpreted differently by laypeople, scientists, and large language models (LLMs), finding that laypeople judge generics as more generalizable and credible than scientists, and LLMs rate them even higher, highlighting risks of overgeneralization.
Scientists often use generics, that is, unquantified statements about whole categories of people or phenomena, when communicating research findings (e.g., "statins reduce cardiovascular events"). Large language models (LLMs), such as ChatGPT, frequently adopt the same style when summarizing scientific texts. However, generics can prompt overgeneralizations, especially when they are interpreted differently across audiences. In a study comparing laypeople, scientists, and two leading LLMs (ChatGPT-5 and DeepSeek), we found systematic differences in interpretation of generics. Compared to most scientists, laypeople judged scientific generics as more generalizable and credible, while LLMs rated them even higher. These mismatches highlight significant risks for science communication. Scientists may use generics and incorrectly assume laypeople share their interpretation, while LLMs may systematically overgeneralize scientific findings when summarizing research. Our findings underscore the need for greater attention to language choices in both human and LLM-mediated science communication.