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Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach

Prince Aboagye, Yan Zheng, Junpeng Wang, Uday Singh Saini, Xin Dai, Michael Yeh, Yujie Fan, Zhongfang Zhuang, Shubham Jain, Liang Wang, Wei Zhang

TL;DR

The paper tackles the challenge of efficiently evaluating pretrained models without exhaustive downstream fine-tuning by introducing a posterior-based metric that quantifies the consistency between entity embeddings and their associated meta-features. The core idea is to partition the embedding space using meta-features (via single-feature clustering, EmbeddingTree segmentation, or MAP-guided 2-GMM splits) and measure the average log posterior (ALP) of embeddings to their clusters, with robustness mechanisms like clipping and multi-head subspaces. Key contributions include the ALP metric, EmbeddingTree-based segmentation, and cross-model embedding comparison under identical splits, demonstrated across synthetic data, MovieLens relational data, Llama-2, and CLIP BREEDS, showing strong alignment with downstream performance. This work offers a scalable, model-agnostic tool for rapid model ranking and benchmarking across modalities, enabling efficient model selection in practice.

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.

Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach

TL;DR

The paper tackles the challenge of efficiently evaluating pretrained models without exhaustive downstream fine-tuning by introducing a posterior-based metric that quantifies the consistency between entity embeddings and their associated meta-features. The core idea is to partition the embedding space using meta-features (via single-feature clustering, EmbeddingTree segmentation, or MAP-guided 2-GMM splits) and measure the average log posterior (ALP) of embeddings to their clusters, with robustness mechanisms like clipping and multi-head subspaces. Key contributions include the ALP metric, EmbeddingTree-based segmentation, and cross-model embedding comparison under identical splits, demonstrated across synthetic data, MovieLens relational data, Llama-2, and CLIP BREEDS, showing strong alignment with downstream performance. This work offers a scalable, model-agnostic tool for rapid model ranking and benchmarking across modalities, enabling efficient model selection in practice.

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.
Paper Structure (21 sections, 12 equations, 16 figures, 14 tables, 1 algorithm)

This paper contains 21 sections, 12 equations, 16 figures, 14 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Pre-trained models
  4. Algorithm Framework
  5. Proposed Method: Posterior Based Embedding Evaluating Metric
  6. One Meta Feature Based Clustering
  7. Meta features + representation based segmentation
  8. 2-GMM Splitting with Maximum A Posteriori Estimation (MAP)
  9. Finding the Best Splitting Point
  10. Embedding Comparison based on the same splitting criteria
  11. Experimental Analysis
  12. Synthetic Dataset: Gaussian Mixture Model (GMM) of Ten Gaussian Distributions
  13. Moive Lens Dataset for Relational
  14. Movie Lens Dataset: Clustering by Year
  15. Movie Lens Dataset: Clustering by Genre
  16. ...and 6 more sections

Figures (16)

  • Figure 1: Illustration on a 2D synthetic dataset consisting of 10 Gaussian distributions that are perfectly separated, partially overlapping, and perfectly overlapping.
  • Figure 2: Mean of the average of the log posterior and accuracy on the MovieLens dataset by clustering on year.
  • Figure 3: Mean of the average of the log posterior and accuracy on the MovieLens dataset by clustering on the genre.
  • Figure 4: Mean of the average of the log posterior and accuracy on the MovieLens dataset by clustering with tree leaf nodes.
  • Figure 5: Embedding quality over model layers (average of the log posterior). The dimensions are divided into subsets, each comprising 128 dimensions.
  • ...and 11 more figures