Generics in science communication: Misaligned interpretations across laypeople, scientists, and large language models

Abstract

, that is, unquantified statements about whole categories of people or phenomena, when communicating research findings (e.g. "statins reduce cardiovascular events"). Large language models, 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 large language models (ChatGPT-5 and DeepSeek), we found systematic differences in interpretation of generics. Compared with most scientists, laypeople judged scientific generics as more generalizable and credible, while large language models rated them even higher. These mismatches highlight significant risks for science communication. Scientists may use generics and incorrectly assume laypeople share their interpretation, while large language models may systematically overgeneralize scientific findings when summarizing research. Our findings underscore the need for greater attention to language choices in both human and large language model-mediated science communication.

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