<ul><li>Automated scoring systems used in large-scale assessment traditionally require a large quantity of hand-scored data for accurate predictions.</li><li>Generative Large Language Models can generalize to new tasks with little to no data but still need fine-tuning.</li><li>The proposed model distillation pipeline, named 'Cyborg Data', combines human and machine-scored responses in training.</li><li>Student models trained on 'Cyborg Data' achieve performance similar to training on the entire dataset, using only 10% of the original hand-scored data.</li></ul>

Cyborg Data: Merging Human with AI Generated Training Data

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