In the rapidly evolving field of artificial intelligence, zero-shot learning has emerged as a compelling paradigm. This approach empowers language models to tackle novel tasks without explicit training data. Nonetheless, accurately evaluating zero-shot performance remains a significant challenge. Conventional evaluation methods often fall short in