科学传播中的泛化：面向非专业人士、科学家和大型语言模型的解释差异
Generics in science communication: Misaligned interpretations across laypeople, scientists, and large language models
AI总结：
研究揭示了科学传播中泛化陈述在不同受众间的解释差异，指出非专业人士和LLM对泛化陈述的高可推广性评分可能带来风险，强调需重视语言选择以提升科学传播效果。
摘要：
科学家在传播研究发现时常常使用泛化陈述，即关于整个人群或现象的未量化陈述（例如，“他汀类药物可减少心血管事件”）。大型语言模型（如ChatGPT）在总结科学文本时也经常采用相同的风格。然而，泛化可能引发过度泛化，尤其是在不同受众对其解释不一致的情况下。在一项比较非专业人士、科学家和两款领先LLM（ChatGPT-5和DeepSeek）的研究中，我们发现了泛化解释的系统性差异。与大多数科学家相比，非专业人士认为科学泛化更具有普遍性和可信度，而LLM的评分甚至更高。这些不匹配突显了科学传播中的重大风险。科学家可能使用泛化并错误地假设非专业人士有相同的理解，而LLM在总结研究时可能系统性地过度泛化科学发现。我们的发现强调了在人类和LLM介导的科学传播中对语言选择给予更多关注的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
