How Does Data Diversity Shape the Weight Landscape of Neural Networks?

To enhance the generalization of machine learning models to unseen data, techniques such as dropout, weight decay ($L_2$ regularization), and noise augmentation are commonly employed. While regularization methods (i.e., dropout and weight decay) are geared toward adjusting model parameters to prevent overfitting, data augmentation increases the diversity of the input training set, a method purported to improve accuracy and calibration error. In this paper, we investigate the impact of each of these techniques on the parameter space of neural networks, with the goal of understanding how they alter the weight landscape in transfer learning scenarios. To accomplish this, we employ Random Matrix Theory to analyze the eigenvalue distributions of pre-trained models, fine-tuned using these techniques but using different levels of data diversity, for the same downstream tasks. We observe that diverse data influences the weight landscape in a similar fashion as dropout. Additionally, we compare commonly used data augmentation methods with synthetic data created by generative models. We conclude that synthetic data can bring more diversity into real input data, resulting in a better performance on out-of-distribution test instances.

Fill In The Gaps: Model Calibration and Generalization with Synthetic Data

As machine learning models continue to swiftly advance, calibrating their performance has become a major concern prior to practical and widespread implementation. Most existing calibration methods often negatively impact model accuracy due to the lack of diversity of validation data, resulting in reduced generalizability. To address this, we propose a calibration method that incorporates synthetic data without compromising accuracy. We derive the expected calibration error (ECE) bound using the Probably Approximately Correct (PAC) learning framework. Large language models (LLMs), known for their ability to mimic real data and generate text with mixed class labels, are utilized as a synthetic data generation strategy to lower the ECE bound and improve model accuracy on real test data. Additionally, we propose data generation mechanisms for efficient calibration. Testing our method on four different natural language processing tasks, we observed an average up to 34\% increase in accuracy and 33\% decrease in ECE.