- The paper establishes that the pre-training and fine-tuning paradigm consistently outperforms self-training methods in semi-supervised learning.
- It details an ensemble method combining various training paradigms and shows limited gain when self-training is added to pre-training.
- It shows that moderate data augmentation improves performance, while excessive augmentation or data imbalance can significantly degrade results.
A Comparative Study of Pre-training and Self-training
The paper "A Comparative Study of Pre-training and Self-training," authored by Yiheng Wang, Jiayu Lin, and Zuoquan Lin, presents a rigorous investigation into the relative efficacy of pre-training and self-training methodologies in semi-supervised learning (SSL). The research aims to elucidate the nuanced differences between these two approaches through a methodical empirical paper using an ensemble of training paradigms.
Introduction
Semi-supervised learning leverages both labeled and unlabeled data to enhance model performance, especially when labeled data is scarce. Pre-training and self-training are two prominent approaches within this paradigm. Pre-training typically involves initial training on a large, unlabeled dataset to learn general representations, followed by fine-tuning on a smaller, labeled dataset. In contrast, self-training involves iteratively training a model on labeled data, then generating pseudo-labels for high-confidence predictions on unlabeled data, which are subsequently used to retrain the model.
Methodology
The paper proposes an exhaustive and comparative ensemble method that incorporates various feasible training paradigms combining pre-training, self-training, and fine-tuning. The primary paradigms investigated include:
- Pre-training and Fine-tuning (PF): Initial pre-training on unlabeled data followed by fine-tuning on labeled data.
- Self-training (S): Iterative training using labeled data and pseudo-labeled data.
- Pre-training followed by Self-training (PS): Starting with pre-training, then employing self-training.
- Pre-training, Fine-tuning, and then Self-training (PFS): Fine-tuning a pre-trained model as the initial teacher for self-training.
- Various combinations including fine-tuning after self-training (SF, PSF, PFSF): To explore if fine-tuning the final student model offers additional benefits.
The paper utilizes BERT-medium and BERT-base models across six datasets encompassing sentiment analysis and natural language inference tasks. Data augmentation strategies of varying intensities, including natural noise, conditional BERT, and back-translation, are employed to assess their impact on model performance.
Experimental Results
The empirical findings of the paper are as follows:
- Superiority of Pre-training and Fine-tuning (PF): The pre-training and fine-tuning paradigm consistently outperforms all other paradigms across various datasets and tasks. This confirms the robustness of the PF approach, aligning with the established efficacy of pre-trained LLMs.
- Limited Benefit of Self-training in Combination with Pre-training: Adding self-training to the pre-training and fine-tuning paradigm (PFS) does not yield performance benefits. This suggests that the fine-tuned model already captures the necessary information from the labeled data, marginalizing the incremental value of self-training.
- Impact of Data Augmentation: Moderate data augmentation enhances performance, while excessive augmentation either stalls improvements or degrades performance. The PF paradigm demonstrates stability under varying intensities of data augmentation, unlike other paradigms which show erratic changes.
- Effects of Data Imbalance: Under imbalanced data scenarios, the PF paradigm shows a modest decline, whereas other paradigms, especially self-training-centric ones, suffer significant performance drops. This accentuates the robustness of the PF approach in real-world scenarios where data imbalance is common.
Discussion
One critical insight is the ineffectiveness of self-training when preceded by pre-training, attributed to suboptimal knowledge transfer via pseudo-labels. However, initializing the student model with pre-trained parameters (PFS Pre-init) significantly mitigates this issue, resulting in improved performance over both PF and S. This underscores the potential of leveraging strong pre-trained models to initialize self-training systems.
Conclusion
The paper conclusively establishes the pre-training and fine-tuning (PF) paradigm as the most effective approach within the current scope of semi-supervised learning. While self-training provides benefits when used independently, its combination with pre-training offers no additional gains, likely due to the redundancy in the information captured during fine-tuning. The empirical robustness of the PF paradigm across varying data augmentation magnitudes and imbalanced datasets further reinforces its applicability in practical scenarios.
The insights from this paper highlight important considerations for future research and application in SSL, particularly emphasizing the pre-training and fine-tuning paradigm's efficacy. Future work may explore more sophisticated self-training mechanisms or alternative methods to mitigate the identified limitations.
References:
- [Van Engelen, Jesse, and Holger H. Hoos. "A survey on semi-supervised learning." Machine Learning 109 (2020): 373-440.]
- [Shi, Hanlin, et al. "Rethinking Continual Pre-training and Self-training." arXiv preprint (Ali et al., 2023) (2023).]
- [Zoph, Barret, et al. "Rethinking pre-training and self-training." Advances in Neural Information Processing Systems 30 (2020): 4324-4335.]
