DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios
arXiv:2606.07226v1 Announce Type: new Abstract: Human creativity has emerged as a critical competency in the era of large language models. Assessing creativity in complex, open-ended environments is a grand challenge in data mining, currently hindered by a reliance on standardized simple tasks and the scarcity of fine-grained expert data. As an ecologically valid assessment context, debate reflects multiple dimensions of creativity, encompassing both divergent thinking and convergent thinking. Moreover, debate is a data-rich domain, with a large volume of publicly accessible materials. Current
The proliferation of large language models necessitates better methods for assessing complex human abilities like creativity, moving beyond simplistic tasks to more ecologically valid contexts.
Improved, fine-grained assessment of creativity in open-ended environments like debates can accelerate AI development, especially for agentic systems, and refine our understanding of human-AI collaboration.
The proposed 'DEFINED' framework offers a data-efficient computational method to objectively measure creativity in complex, unstructured scenarios, potentially enabling more sophisticated evaluation metrics for AI models and human skills alike.
- · AI researchers
- · Educational assessment platforms
- · Talent management
- · Debate organizations
- · Traditional creativity assessment tools
- · Organizations relying solely on standardized testing
More robust and generalizable AI models, particularly AI agents, could emerge from better creativity assessment.
The ability to accurately quantify creativity might lead to new paradigms in education, training, and human capital development.
Enhanced understanding and measurement of creativity could inform policy on how AI impacts future workforce skills and economic resilience.
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