Language models (LMs) show promise as tools for communicating science to the general public by simplifying and summarizing complex language. Because models can be prompted to generate text for a specific audience (e.g., college-educated adults), LMs might be used to create multiple versions of plain language summaries for people with different familiarities of scientific topics. However, it is not clear what the benefits and pitfalls of adaptive plain language are. When is simplifying necessary, what are the costs in doing so, and do these costs differ for readers with different background knowledge? Through three within-subjects studies in which we surface summaries for different envisioned audiences to participants of different backgrounds, we found that while simpler text led to the best reading experience for readers with little to no familiarity in a topic, high familiarity readers tended to ignore certain details in overly plain summaries (e.g., study limitations). Our work provides methods and guidance on ways of adapting plain language summaries beyond the single "general" audience.

翻译：语言模型（LM）在通过简化与总结复杂语言向公众传播科学知识方面展现出潜力。由于模型可被提示为特定受众（如受过大学教育的成年人）生成文本，LM或可用于为具有不同科学主题熟悉度的人群创建多个版本的简明语言摘要。然而，自适应简明语言的优势与隐患尚不明确：何时需要简化？简化需付出何种成本？这些成本是否会因读者背景知识的差异而不同？通过三项被试内实验，我们向具有不同背景的参与者呈现为不同设想受众生成的摘要，结果发现：尽管简化的文本能为对主题几乎或完全陌生的读者提供最佳阅读体验，但高熟悉度读者往往会忽略过度简化摘要中的某些细节（如研究局限性）。本研究为超越单一“普通”受众的简明语言摘要自适应方法提供了实践指导与理论依据。