DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios

A new computational framework called DEFINED aims to automate the assessment of creativity in debate, a task that has largely relied on costly human evaluation. The model, detailed in a paper submitted to arXiv, breaks debate creativity into a hierarchical eight-dimensional metric system. [1] The framework addresses a persistent challenge in data mining: evaluating creativity in complex, open-ended environments. Current automated scoring methods are poorly suited to nuanced settings like debate, which require assessing both divergent and convergent thinking. [1] The researchers note that debate offers a data-rich domain with a large volume of publicly accessible materials, making it an ecologically valid context for study. [1] DEFINED was built using a pre-trained autoregressive language model, a type of neural network foundational to modern chatbots and text generation tools. [7] The model was fitted with a hierarchical scoring head capable of producing both fine-grained and coarse-grained evaluations. [1] Training data consisted of statements and expert scores drawn from authentic debate competitions. To counter an elite bias in the original data, the team employed a constrained data augmentation strategy. [1] The scarcity of fine-grained expert data is a known bottleneck in the field. To overcome this, DEFINED uses a mixed-granularity training strategy, allowing it to learn robustly from a limited set of annotations provided by trained graduate experts. [1] The paper, posted on the open-access repository arXiv, has not yet been peer-reviewed, a standard caveat for e-prints on the platform, which has hosted scientific preprints since 1991 and now receives about 24,000 submissions per month. [5] Beyond synthetic benchmarks, the researchers conducted an empirical study with debate-naive participants to validate the model's ecological validity for mid-to-low proficiency populations. [1] In their evaluation protocol, the DEFINED scoring model outperformed both prompt-based large language model evaluators and existing debate scoring methods, achieving what the authors describe as accurate and stable scoring. [1] The work was submitted in June 2026 and is associated with experimental projects from arXivLabs and the machine learning platform Hugging Face. [1]