SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (1911.03437v5)

Abstract: Transfer learning has fundamentally changed the landscape of NLP research. Many existing 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 large capacity of pre-trained models, aggressive fine-tuning often causes the adapted model to overfit the data of downstream tasks and forget the knowledge of the pre-trained model. To address the above issue in a more principled manner, we propose a new computational framework for robust and efficient fine-tuning for pre-trained LLMs. Specifically, our proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the capacity of the model; 2. Bregman proximal point optimization, which is a class of trust-region methods and can prevent knowledge forgetting. Our experiments demonstrate that our proposed method achieves the state-of-the-art performance on multiple NLP benchmarks.

SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural LLMs through Principled Regularized Optimization

The paper "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural LLMs through Principled Regularized Optimization" presents a novel framework aimed at improving the fine-tuning process of pre-trained LLMs in NLP. This framework, named SMART, integrates two principal components — smoothness-inducing adversarial regularization and Bregman proximal point optimization — to address overfitting and aggressive updating commonly encountered in fine-tuning scenarios.

Methodology

The SMART framework seeks to control model complexity and enhance generalization by employing:

1. Smoothness-Inducing Adversarial Regularization: This component manages model complexity through local smoothness enforcement, ensuring that small perturbations in input data do not result in large changes in model output. The regularization is derived from robust statistics literature, specifically focusing on local Lipschitz continuity.
2. Bregman Proximal Point Optimization: To prevent aggressive updates during fine-tuning, this optimization method incorporates trust-region-type updates that keep changes within a small neighborhood of the previous parameters. This method anchors updates, retaining valuable learned knowledge.

Experimental Results

The authors conducted comprehensive experiments on several NLP benchmarks, including GLUE, SNLI, SciTail, and ANLI, achieving state-of-the-art results. Notably, SMART surpassed the performance of the T5 model, which contains 11 billion parameters on the GLUE benchmark, with a leaner model of only 356 million parameters.

The analysis of GLUE results indicates significant performance improvements, especially on tasks with smaller datasets such as RTE and MRPC, where overfitting is more pronounced. The method consistently outperformed existing baselines, providing a robust solution when transitioning from a general pre-trained state to a task-specific model.

Contributions and Implications

The proposed approach contributes significantly in several aspects:

• It introduces a novel adversarial regularization technique tailored for fine-tuning LLMs, ensuring better generalization.
• By incorporating the proximal point method, it provides a principled way of preventing aggressive updates.
• The framework demonstrates potential applications beyond standard NLP tasks, suggesting utility in domain adaptation and robustness to adversarial attacks.

The methodology presents a promising direction for future research, especially in exploring extensions to other transfer learning scenarios.

Future Directions

The paper opens up several future research avenues:

• Extending the SMART framework to other modalities beyond NLP, such as vision or multi-modal tasks.
• Investigating the integration of SMART with multi-task learning approaches to assess potential synergistic effects on model performance.
• Fine-tuning hyperparameters and exploring alternative regularization strategies to further reduce computational overhead while maintaining model robustness.

In conclusion, the SMART framework offers a critical advancement in fine-tuning methodologies, balancing complexity management with effective learning, and advocating for more structured, principled approaches in transfer learning for NLP models. This work sets a benchmark for future innovations in model fine-tuning and systematic optimization approaches.