利用多臂老虎机和GPT-4学习交互以提升人类创造力
Learning interactions to boost human creativity with bandits and GPT-4
AI总结：
本文利用多臂老虎机学习人机交互中的提示策略，在语义特征生成任务中提升人类与GPT-4的创造力，发现AI与人类偏好一致，且策略可迁移至模拟群体。
摘要：
本文探讨了与AI算法的交互如何提升人类的创造性思维。我们采用了一项心理学任务来展示人类创造力的局限性，即语义特征生成：给定一个概念名称，受访者必须尽可能多地列出其特征。人类参与者通常只能生成他们所知特征的一小部分，然后就会“卡住”。在人类和语言AI（GPT-4）的实验中，我们对比了标准任务与一种变体任务中的行为，在变体任务中，参与者可以请求算法生成的提示。算法选择由多臂老虎机管理，其奖励指示提示是否有助于生成更多特征。人类和AI从提示中获得的收益相似，值得注意的是，从AI响应中学习的老虎机与从人类行为中学习的老虎机偏好相同的提示策略。结果表明，通过计算机交互提升人类创造力的策略可以通过在模拟参与者群体上运行的老虎机来学习。
英文摘要：
This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely semantic feature generation: given a concept name, respondents must list as many of its features as possible. Human participants typically produce only a fraction of the features they know before getting "stuck." In experiments with humans and with a language AI (GPT-4) we contrast behavior in the standard task versus a variant in which participants can ask for algorithmically-generated hints. Algorithm choice is administered by a multi-armed bandit whose reward indicates whether the hint helped generating more features. Humans and the AI show similar benefits from hints, and remarkably, bandits learning from AI responses prefer the same prompting strategy as those learning from human behavior. The results suggest that strategies for boosting human creativity via computer interactions can be learned by bandits run on groups of simulated participants.
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。
