Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sánchez-Batet) and semantic similarity (Lin/Sánchez-Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence.
CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
Southern University of Science and Technology（南方科技大学）；Tsinghua University（清华大学）；Peking University（北京大学）；University of Bristol（布里斯托大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong University（西安交通大学）
Applying Text Mining to Analyze Human Question Asking in Creativity Research
Faculty of Mathematics and Information Sciences, Warsaw University of Technology（华沙技术大学数学与信息科学学院）；Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology（技术学院-以色列理工学院数据与决策科学学院）；School of Applied Data Science, Modul University Vienna（维也纳模尔大学应用数据科学学院）；Systems Research Institute Polish Academy of Sciences（波兰科学院系统研究 institute）
Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas \textit{outside} such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To t…
AI large language models have (co-)produced amazing written works from newspaper articles to novels and poetry. These works meet the standards of the standard definition of creativity: being original and useful, and sometimes even the additional element of surprise. But can a large language model designed to predict the next text fragment provide creative, out-of-the-box, responses that still solve the problem at hand? We put Open AI's generative natural language model, GPT-3, to the test. Can it provide creative solutions to one of the most commonly used tests in creativity research? We asses…
Probing and Inducing Combinational Creativity in Vision-Language Models
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Yuanpei College, Peking University（北京大学元培学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（人工智能通用基础理论国家重点实验室）；Center on Frontiers of Computing Studies, School of Computer Science, Peking University（计算机科学学院前沿计算研究中心）
Dynamic semantic networks for exploration of creative thinking
Institute for Advanced Study（高等研究院）；Center for Ubiquitous Computing（普适计算中心）；Faculty of Information Technology and Electrical Engineering（信息技术与电气工程学院）；University of Oulu（奥卢大学）
Large Language Models Align with the Human Brain during Creative Thinking
EPFL（瑞士联邦理工学院洛桑）；Università della Svizzera italiana (USI)（意大利语区大学（瑞士））；Wesleyan University（卫斯理大学）；Paris Brain Institute (ICM)（巴黎大脑研究所）；Pennsylvania State University（宾夕法尼亚州立大学）
Divergent Creativity in Humans and Large Language Models
Université de Montréal（蒙特利尔大学）；Concordia University（康考迪亚大学）；Mila (Quebec AI research Institute)（米拉（魁北克人工智能研究所））；University of Toronto Mississauga（多伦多大学密西沙加分校）；UNIQUE Center (Quebec Neuro-AI research Center)（UNIQUE中心（魁北克神经人工智能研究中心））
Inducing Sustained Creativity and Diversity in Large Language Models
Department of East Asian Languages and Civilizations, Harvard University（哈佛大学东亚语言与文明系）；Institute for Quantitative Social Science, Harvard University（哈佛大学定量社会科学研究所）；Harvard University（哈佛大学）
Instituto de Telecomunicações (IT-IUL)（电信研究所（IT-IUL））；Instituto Universitário de Lisboa (Iscte-IUL)（里斯本大学学院（Iscte-IUL））
We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to impr…
