对比人类与大模型对情绪词的联想，发现模型更一致但缺乏创意。
The truth is no diaper: Human and AI-generated associations to emotional words
- 比较人类与大模型对情绪词的自发联想模式。
- 模型联想更可预测，但情感强度被放大，创造力低于人类。
- 适合研究认知机制或生成内容可信度的读者。
人类词语联想是了解内在心理词汇的重要方法，但个体经历、情绪和认知风格会影响其响应，导致结果不可预测。在看似无关概念间建立关联的能力，是创造力的核心驱动力。本文比较了人类与大型语言模型（LLMs）对情绪化词语的联想行为，旨在判断大模型是否以类似人类的方式生成联想。研究发现，人类与大模型的联想重合度中等，但大模型的联想往往强化刺激词的潜在情感负荷，且更具可预测性，创造性则低于人类。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。