#zero-shot-learning

#zero-shot-learning

Concept frequency serves as a significant predictor for the zero-shot performance of models. The statistical analysis shows that varying frequencies directly impact the model's predictive capabilities in a consistent manner, regardless of filtering data types.

The strong log-linear trend between concept frequency and zero-shot performance consistently holds across different prompting strategies, indicating that more frequently encountered concepts in pretraining data yield better performance.