真相不是尿布：人类与AI对情感词产生的联想
The truth is no diaper: Human and AI-generated associations to emotional words
AI总结：
本文比较人类与大型语言模型对情感词的联想行为，发现两者重叠中等，LLM的联想更可预测、更缺乏创造性且会放大情感负荷。
摘要：
人类词汇联想是一种众所周知的洞察内部心理词典的方法，但人类参与者对词语提示自发给出的反应并不总是可预测的，因为它们可能受到个人经历、情绪或个体认知风格的影响。在看似无关的概念之间形成联想链接的能力可能是创造力的驱动机制。我们对人类与大型语言模型的联想行为进行了比较。更具体地说，我们探索了对带有情感色彩的词的联想，并试图确定大型语言模型是否以与人类相似的方式生成联想。我们发现人类与LLM之间的重叠是中等的，但LLM的联想倾向于放大刺激词潜在的情感负荷，并且它们往往比人类的联想更可预测、更缺乏创造性。
英文摘要：
Human word associations are a well-known method of gaining insight into the internal mental lexicon, but the responses spontaneously offered by human participants to word cues are not always predictable as they may be influenced by personal experience, emotions or individual cognitive styles. The ability to form associative links between seemingly unrelated concepts can be the driving mechanisms of creativity. We perform a comparison of the associative behaviour of humans compared to large language models. More specifically, we explore associations to emotionally loaded words and try to determine whether large language models generate associations in a similar way to humans. We find that the overlap between humans and LLMs is moderate, but also that the associations of LLMs tend to amplify the underlying emotional load of the stimulus, and that they tend to be more predictable and less creative than human ones.
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。
