生成式人工智能科普前沿科学成果的特点研究——基于中国主要大模型生成内容与中国科学报化学领域相关报道的对比

A Study on the Characteristics of Frontier Scientific Achievements in GenAI Science Popularization: A Comparison Between Content Generated by Major Chinese Large Models and Chemistry-Related Reports in China Science Daily

中文关键词: 生成式人工智能 前沿科学成果 科普 特点

英文关键词: GenAI  frontier scientific achievements  science popularization  characteristics

中文摘要:

      前沿科学成果科普对于公众理解和参与科学都具有重要意义。随着生成式人工智能的广泛应用，是否能够将其用于前沿科学成果科普过程已经成为一个重要的主题。和生成一般科学传播内容相比，生成式人工智能在前沿科学成果相关主题上具有独特之处：一方面缺少足够多可以用作训练样本的网络文本，另一方面具有相对较高的不确定性，因此需要更谨慎的内容表达方式。本研究通过对比传统科普内容生成者和生成式人工智能模型产出的前沿科学成果科普内容，发现两者在科学准确性和信源可靠性方面并没有显著差别，但是生成式人工智能模型产出科普内容要比传统科普内容生成者产出的内容更倾向于呈现出正面情感。基于此，本研究提出，生成式人工智能在错误信息产出方面的风险可能并不大，但是却可能进一步加强已有的刻板印象，因此生成式人工智能并不能替代已有科普系统中的传统科普内容生成者。 [关键词]生成式人工智能 前沿科学成果 科普 特点

英文摘要:

      The popularization of frontier scientific achievements plays a pivotal role in enhancing public understanding and engagement with science. With the widespread application of generative artificial intelligence (GenAI), its potential performance of popularizing frontier scientific achievements has become a critical topic. Unlike the generation of general science communication content, GenAI encounters distinct challenges when addressing topics related to frontier scientific achievements: firstly, there is a paucity of sufficient online textual data for GenAI training; secondly, these topics often entail relatively higher uncertainty, necessitating a more cautious approach to content formulation. This study conducts a comparative analysis of popular science content on frontier scientific achievements generated by traditional science popularizers and that produced by GenAI models. The results indicate no significant disparities between the two in terms of scientific accuracy and source reliability. However, the content generated by GenAI models tends to exhibit a more positive emotional tone compared to that produced by traditional science popularizers. Based on these results, this study suggests that while the risk of GenAI disseminating misinformation may be limited, it has the potential to reinforce existing stereotypes. Consequently, GenAI cannot entirely supplant traditional science popularizers within the current framework of science popularization.