8 citations · 16 across the 4 of their papers we have counts for
5 papers
Augmenting Rating-Scale Measures with Text-Derived Items Using the Information-Determined Scoring (IDS) Framework
Joe Watson, Ivan O'Connor, Chia-Wen Chen +3
Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories. Although rich, unstructured tex…
Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator
Qian Cao, Xiting Wang, Yuzhuo Yuan +3
Creativity evaluation remains a challenging frontier for large language models (LLMs). Current evaluations heavily rely on inefficient and costly human judgments, hindering progres…
Large Language Models show both individual and collective creativity comparable to humans
Luning Sun, Yuzhuo Yuan, Yuan Yao +6
Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to hum…
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Cheng Li, Jindong Wang, Yixuan Zhang +7
Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various t…
Evaluating General-Purpose AI with Psychometrics
Xiting Wang, Liming Jiang, Jose Hernandez-Orallo +4
Comprehensive and accurate evaluation of general-purpose AI systems such as large language models allows for effective mitigation of their risks and deepened understanding of their…