Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs (commercial and open-source) to science questions when prompted to target different age groups and education levels. To assess the adaptability of LLMs to diverse audiences, we compare the readability scores of the generated responses against the recommended comprehension level of each age and education group. We find large variations in the readability of the answers by different LLMs. Our results suggest LLM answers need to be better adapted to the intended audience demographics to be more comprehensible. They underline the importance of enhancing the adaptability of LLMs in education settings to cater to diverse age and education levels. Overall, current LLMs have set readability ranges and do not adapt well to different audiences, even when prompted. That limits their potential for educational purposes.

翻译：大语言模型（LLMs）提供了诸多新可能性，包括根据受众及其阅读需求调整文本内容。但它们的适应能力究竟如何？我们评估了四种最先进的大语言模型（包括商用和开源模型）在针对不同年龄组和教育水平进行提示时，生成科学问题答案的可读性。为检测大语言模型对不同受众的适应能力，我们将生成答案的可读性评分与各年龄及教育群体推荐的阅读理解水平进行比较。研究发现不同大语言模型生成答案的可读性存在显著差异。结果表明，大语言模型需更好地针对目标受众人口统计特征调整答案，以提升其可理解性。这一发现凸显了在教育场景中增强大语言模型适应能力的重要性，以便满足不同年龄与教育水平的需求。总体而言，当前大语言模型具有固定的可读性范围，即使通过提示引导，也难以适应不同受众群体，这限制了其在教育领域的应用潜力。