Curiosity serves as a pivotal conduit for human beings to discover and learn new knowledge. Recent advancements of large language models (LLMs) in natural language processing have sparked discussions regarding whether these models possess capability of curiosity-driven learning akin to humans. In this paper, starting from the human curiosity assessment questionnaire Five-Dimensional Curiosity scale Revised (5DCR), we design a comprehensive evaluation framework that covers dimensions such as Information Seeking, Thrill Seeking, and Social Curiosity to assess the extent of curiosity exhibited by LLMs. The results demonstrate that LLMs exhibit a stronger thirst for knowledge than humans but still tend to make conservative choices when faced with uncertain environments. We further investigated the relationship between curiosity and thinking of LLMs, confirming that curious behaviors can enhance the model's reasoning and active learning abilities. These findings suggest that LLMs have the potential to exhibit curiosity similar to that of humans, providing experimental support for the future development of learning capabilities and innovative research in LLMs.

翻译：好奇心是人类发现和学习新知识的关键通道。近年来，大型语言模型在自然语言处理领域的进展引发了关于这些模型是否具备类似人类好奇心驱动学习能力的讨论。本文从人类好奇心评估问卷《五维好奇心量表修订版》出发，设计了一个涵盖信息寻求、刺激寻求、社交好奇心等维度的综合评估框架，用以衡量大型语言模型所展现的好奇心程度。结果表明，大型语言模型表现出比人类更强烈的求知欲，但在面对不确定环境时仍倾向于做出保守选择。我们进一步探究了好奇心与大型语言模型思维之间的关系，证实好奇行为能够增强模型的推理与主动学习能力。这些发现表明大型语言模型具备展现类人好奇心的潜力，为其未来学习能力的发展与创新研究提供了实验依据。