Evaluating Natural Language Processing Models with Generalization Metrics that Do Not Need Access to Any Training or Testing Data

By Y. Yang et al.

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Table of Contents

Abstract
1 Introduction
2 Generalization Metrics for Model Selection in NLP
3 Heavy-tail Self-regularization
4 Preliminary of ESDs of Weight Matrices
5 Contributions
6 Issues of PL Fitting
7 Comparing PL and E-TPL Fitting

Summary

This document discusses the evaluation of natural language processing models with generalization metrics that do not require access to training or testing data. It focuses on the importance of selecting suitable architecture parameters and training hyperparameters to enhance machine learning model performance. The paper explores metrics that can guide model selection without the need for data and examines how generalization-metric-based model selection can be applied to large pretrained Transformers from Huggingface. The study considers various generalization metrics and their correlations with model quality, emphasizing the importance of metrics that can predict trends in test error directly. The document also delves into the heavy-tail self regularization theory and shape metrics derived from it, providing insights into fitting distributions to weight matrices. Overall, the paper contributes to a systematic analysis of generalization metrics in NLP and highlights the effectiveness of metrics derived from HT-SR theory for model selection.