Has Your Pretrained Model Improved? A Multi-head Posterior Based
Approach
- URL: http://arxiv.org/abs/2401.02987v4
- Date: Wed, 14 Feb 2024 19:05:46 GMT
- Title: Has Your Pretrained Model Improved? A Multi-head Posterior Based
Approach
- Authors: Prince Aboagye, Yan Zheng, Junpeng Wang, Uday Singh Saini, Xin Dai,
Michael Yeh, Yujie Fan, Zhongfang Zhuang, Shubham Jain, Liang Wang and Wei
Zhang
- Abstract summary: 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 pre-trained models.
Our method's effectiveness is demonstrated across various domains, including models with relational datasets, large language models and image models.
- Score: 25.927323251675386
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The emergence of pre-trained models has significantly impacted 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 pre-trained models. Our method's effectiveness is
demonstrated across various domains, including models with relational datasets,
large language models and image models.
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