前沿大语言模型适合科学中心的问答吗？
Are Frontier Large Language Models Suitable for Q&A in Science Centres?
AI总结：
论文基于国家航天中心问题数据集评估 GPT-4、Claude 3.5 Sonnet 与 Gemini 1.5 的儿童科普问答，发现创意会削弱事实可靠性，Claude 综合表现最佳。
摘要：
本文研究前沿大语言模型（LLM）是否适合科学中心的问答互动，目标是在保持事实准确性的同时提升参观者参与度。我们利用从英国莱斯特国家航天中心收集的问题数据集，评估了三个领先模型生成的回答：OpenAI 的 GPT-4、Claude 3.5 Sonnet 和 Google Gemini 1.5。每个模型都被要求为 8 岁受众生成量身定制的标准回答和创意回答；这些回答由空间科学专家依据准确性、参与度、清晰度、新颖性以及与预期答案的偏差进行评估。结果揭示了创造性与准确性之间的权衡：即使在被要求生成更具创意的回答时，Claude 在保持清晰度和吸引年轻受众方面仍优于 GPT 和 Gemini。尽管如此，专家观察到，在所有模型中，更高的新颖性通常伴随着事实可靠性的下降。本研究凸显了 LLM 在教育场景中的潜力，并强调需要谨慎进行提示工程，以在参与感和科学严谨性之间取得平衡。
英文摘要：
This paper investigates the suitability of frontier Large Language Models (LLMs) for Q&A interactions in science centres, with the aim of boosting visitor engagement while maintaining factual accuracy. Using a dataset of questions collected from the National Space Centre in Leicester (UK), we evaluated responses generated by three leading models: OpenAI's GPT-4, Claude 3.5 Sonnet, and Google Gemini 1.5. Each model was prompted for both standard and creative responses tailored to an 8-year-old audience, and these responses were assessed by space science experts based on accuracy, engagement, clarity, novelty, and deviation from expected answers. The results revealed a trade-off between creativity and accuracy, with Claude outperforming GPT and Gemini in both maintaining clarity and engaging young audiences, even when asked to generate more creative responses. Nonetheless, experts observed that higher novelty was generally associated with reduced factual reliability across all models. This study highlights the potential of LLMs in educational settings, emphasizing the need for careful prompt engineering to balance engagement with scientific rigor.
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。
