探究大语言模型的创造力：模型能否产生发散性语义关联？
Probing the Creativity of Large Language Models: Can models produce divergent semantic association?
AI总结：
本研究通过发散性关联任务（DAT）从认知视角评估大语言模型的创造力，发现GPT-4和GPT-3.5-turbo在语义发散上超越人类平均水平，且随机采样与温度缩放可提升除GPT-4外模型的DAT得分，但需权衡稳定性。
摘要：
大语言模型具备处理语言的卓越能力，但这些模型能否进一步生成创造性内容仍不清楚。本研究旨在从认知视角探究大语言模型的创造性思维。我们采用发散性关联任务（DAT），这是一种客观的创造力测量方法，要求模型生成不相关的词语并计算它们之间的语义距离。我们比较了不同模型和不同解码策略下的结果。研究发现：（1）在使用贪心搜索策略时，GPT-4 的表现优于 96% 的人类，而 GPT-3.5-turbo 则超过人类平均水平。（2）随机采样和温度缩放能有效提高除 GPT-4 以外模型的 DAT 得分，但面临创造力与稳定性之间的权衡。这些结果表明，先进的大语言模型具有发散性语义关联能力，这是创造力的基本过程。
英文摘要：
Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content. The present study aims to investigate the creative thinking of large language models through a cognitive perspective. We utilize the divergent association task (DAT), an objective measurement of creativity that asks models to generate unrelated words and calculates the semantic distance between them. We compare the results across different models and decoding strategies. Our findings indicate that: (1) When using the greedy search strategy, GPT-4 outperforms 96% of humans, while GPT-3.5-turbo exceeds the average human level. (2) Stochastic sampling and temperature scaling are effective to obtain higher DAT scores for models except GPT-4, but face a trade-off between creativity and stability. These results imply that advanced large language models have divergent semantic associations, which is a fundamental process underlying creativity.
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