DEFINED: New Framework for Assessing Creativity in Debate Using AI
Researchers have proposed DEFINED, a computational framework that uses a pre-trained language model to assess human creativity in debate scenarios across eight fine-grained dimensions. The system addresses a key bottleneck in creativity assessment—the scarcity of expert-labeled data—through constrained data augmentation and a mixed-granularity training strategy. The work, accepted at KDD 2026, outperforms both prompt-based large language models and existing debate scoring methods, potentially reducing reliance on costly human evaluation.
DEFINED (a Data-Efficient Framework for Fine-grained creativity assessment in debate scenarios) operationalizes creativity through a hierarchical eight-dimensional metric system covering both divergent and convergent thinking. The model uses a pre-trained autoregressive language model with a hierarchical scoring head capable of both fine-grained and coarse-grained evaluation. Training data were drawn from authentic debate competitions, with expert scores annotated by trained graduate students; a constrained data augmentation strategy was applied to mitigate elite bias in the original dataset. A mixed-granularity training strategy allows the model to learn robustly even when detailed supervision is limited. Ecological validity was tested through an empirical study involving debate-naive participants, serving as a qualitative case study for mid-to-low proficiency populations. The framework outperformed prompt-based LLM evaluators and prior debate scoring approaches across the evaluation protocol, suggesting it could substantially lower the cost of large-scale creativity assessment.
What's missing
The paper does not detail inter-rater reliability among the graduate expert annotators, which is important for establishing the ground-truth label quality. It is also unclear how well the eight-dimensional metric system generalizes to debate formats or languages beyond those represented in the training data. The study's reliance on a single domain (debate) leaves open whether the framework transfers to other open-ended creative tasks.
What different sources said
- arXiv cs.CLCenter
DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios
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