Transfer learning has fundamentally changed
the landscape of natural language processing
(NLP). Many state-of-the-art models are first
pre-trained on a large text corpus and then
fine-tuned on downstream tasks. However,
due to limited data resources from downstream
tasks and the extremely high complexity of
pre-trained models, aggressive fine-tuning of-
ten causes the fine-tuned model to overfit the
training data of downstream tasks and fail to
generalize to unseen data. To address such an
issue in a principled manner, we propose a new
learning framework for robust and efficient
fine-tuning for pre-trained models to attain
better generalization performance. The pro-
posed framework contains two important in-
gredients: 1. Smoothness-inducing regulariza-
tion, which effectively manages the complex-
ity of the model; 2. Bregman proximal point
optimization, which is an instance of trust-
region methods and can prevent aggressive up-
dating. Our experiments show that the pro-
posed framework achieves new state-of-the-art
performance on a number of NLP tasks includ-
ing GLUE, SNLI, SciTail and ANLI. More-
over, it also outperforms the state-of-the-art T5
model, which is the largest pre-trained model
containing 11 billion parameters, on GLUE.
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This content will become publicly available on March 11, 2025
On the Generalization Ability of Unsupervised Pretraining
Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset affects the generalization of the fine-tuned model is lacking. Existing theoretical research does not adequately account for the heterogeneity of the distribution and tasks in pre-training and fine-tuning stage. To bridge this gap, this paper introduces a novel theoretical framework that illuminates the critical factor influencing the transferability of knowledge acquired during unsupervised pre-training to the subsequent fine-tuning phase, ultimately affecting the generalization capabilities of the fine-tuned model on downstream tasks. We apply our theoretical framework to analyze generalization bound of two distinct scenarios: Context Encoder pre-training with deep neural networks and Masked Autoencoder pre-training with deep transformers, followed by fine-tuning on a binary classification task. Finally, inspired by our findings, we propose a novel regularization method during pre-training to further enhances the generalization of fine-tuned model. Overall, our results contribute to a better understanding of unsupervised pre-training and fine-tuning paradigm, and can shed light on the design of more effective pre-training algorithms.
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- PAR ID:
- 10542590
- Publisher / Repository:
- International Conference on Artificial Intelligence and Statistics
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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