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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