5 citations · 7 across the 9 of their papers we have counts for
9 papers · 1 filter
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior
Kibum Moon, Dan R. Johnson, Kostadin Kushlev +6
AI-generated outputs are frequently perceived as highly creative, but also tend to be more similar to each other (homogenized) than human ideas. Does the human heterogeneity that e…
AGC-Bench: Measuring Artificial General Creativity
Roger Beaty, Vijeta Deshpande, Clin K. Y. Lai +9
Creativity research has debated whether creativity is domain-specific (e.g., visual, writing, science), and if it is psychometrically separable from general intelligence. Both ques…
How LLMs See Creativity: Zero-Shot Scoring of Visual Creativity with Interpretable Reasoning
William Orwig, Roger E. Beaty
Evaluating the originality of visual images poses enduring challenges for creativity assessment. Automated scoring using AI models has proven effective in the verbal domain, yet ke…
Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
Edith Haim, Kurt Haim, Roger E. Beaty +2
Creativity is a complex cognitive ability that relies on knowledge organisation and retrieval from semantic memory. Yet most research uses a single task to measure it, capturing on…
Playing with Words, Improving with Rewards: Training Language Models for Creative Association
Vijeta Deshpande, Namrata Shivagunde, Sherin Muckatira +5
Large Language Models (LLMs) are being applied to increasingly difficult problems and use cases. To navigate their vast solution spaces effectively, LLMs need to be creative. Yet t…
Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models
Vijeta Deshpande, Debasmita Ghose, John D. Patterson +2
Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models…
Creative Preference Optimization
Mete Ismayilzada, Antonio Laverghetta, Simone A. Luchini +4
While Large Language Models (LLMs) have demonstrated impressive performance across natural language generation tasks, their ability to generate truly creative content-characterized…
How do Humans and Language Models Reason About Creativity? A Comparative Analysis
Antonio Laverghetta, Tuhin Chakrabarty, Tom Hope +3
Creativity assessment in science and engineering is increasingly based on both human and AI judgment, but the cognitive processes and biases behind these evaluations remain poorly…
The creative psychometric item generator: a framework for item generation and validation using large language models
Antonio Laverghetta, Simone Luchini, Averie Linell +2
Increasingly, large language models (LLMs) are being used to automate workplace processes requiring a high degree of creativity. While much prior work has examined the creativity o…