Learning interactions to boost human creativity with bandits and GPT-4
Ara Vartanian Xiaoxi Sun Yun-Shiuan Chuang Siddharth Suresh Xiaojin Zhu Timothy T. Rogers
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.