The emergence of pretrained models has significantly impacted from Natural
Language Processing (NLP) and Computer Vision to relational datasets.
Traditionally, these models are assessed through fine-tuned downstream tasks.
However, this raises the question of how to evaluate these models more
efficiently and more effectively. In this study, we explore a novel approach
where we leverage the meta features associated with each entity as a source of
worldly knowledge and employ entity representations from the models. We propose
using the consistency between these representations and the meta features as a
metric for evaluating pretrained models. Our method's effectiveness is
demonstrated across various domains, including models with relational datasets,
large language models and images models.

通过利用元特征和模型提取的实体表示之间的一致性作为衡量预训练模型的度量标准，本研究探索了一种评估预训练模型更有效且更高效的方法，该方法在包括有关关系数据集的模型、大型语言模型和图像模型的各个领域中得到了有效验证。