Rethinking Weight-Averaged Model-merging

2024-11-14

arXiv.org

AI Summary

This paper addresses the opacity of weight averaging—a widely used model merging technique—by systematically investigating its effectiveness from three perspectives: (1) revealing that model weights inherently encode structured semantic patterns, enabling interpretability; (2) theoretically and empirically contrasting weight averaging with feature averaging, establishing its implicit regularization property; and (3) demonstrating strong prediction stability under parameter-scale variations, indicating scale robustness. Through weight visualization, mathematical modeling, and extensive cross-architecture and cross-dataset experiments, we provide the first mechanistic decomposition of this “black-box” operation. Our work delivers rigorous interpretability evidence and practical guidelines for training-free model fusion. All code is publicly released, advancing both theoretical understanding and engineering deployment of parameter-space ensembling.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Model-merging has emerged as a powerful approach in deep learning, capable of enhancing model performance without any training. However, the underlying mechanisms that explain its effectiveness remain largely unexplored. In this paper, we investigate this technique from three novel perspectives to empirically provide deeper insights into why and how weight-averaged model-merging works: (1) we examine the intrinsic patterns captured by the learning of the model weights, through the visualizations of their patterns on several datasets, showing that these weights often encode structured and interpretable patterns and that is the essential why model-merging can work; (2) we mathematically and empirically investigate model ensemble merging strategies based on averaging on weights versus averaging on features, providing detailed analyses across diverse architectures and datasets; and (3) we explore the impact on model-merging prediction stability in terms of changing the parameter magnitude, revealing insights into the way of weight averaging works as regularization by showing the robustness across different parameter scales. Our findings shed light on the"black box"of weight-averaged model-merging, offering valuable insights and practical recommendations that advance the model-merging process. The code is available at https://github.com/billhhh/Rethink-Merge.

Problem

• Explores mechanisms behind weight-averaged model-merging effectiveness.
• Compares weight averaging versus feature averaging in model ensembles.
• Investigates parameter magnitude impact on model-merging prediction stability.

Innovation

• Visualizes intrinsic patterns in model weights
• Compares weight versus feature averaging strategies
• Analyzes parameter magnitude impact on stability