Original Paper: https://arxiv.org/abs/2303.02577

By: Stephen Obadinma, Hongyu Guo, Xiaodan Zhu

Abstract:

Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is comparable or superior to fine-tuning while dramatically reducing trainable parameters. Nevertheless, the effectiveness of such methods under the context of data augmentation, a common strategy to improve learning under low data regimes, has not been fully explored. In this paper, we examine the effectiveness of several popular task-agnostic data augmentation techniques, i.e., EDA, Back Translation, and Mixup, when using two general parameter efficient tuning methods, P-tuning v2 and LoRA, under data scarcity. We show that data augmentation can be used to boost the performance of P-tuning and LoRA models, but the effectiveness of each technique varies and certain methods can lead to a notable degradation in performance, particularly when using larger models and on harder tasks. We further analyze the sentence representations of P-tuning compared to fine-tuning to help understand the above behaviour, and reveal how P-tuning generally presents a more limited ability to separate the sentence embeddings from different classes of augmented data. In addition, it displays poorer performance on heavily altered data. However, we demonstrate that by adding a simple contrastive loss function it can help mitigate such issues for prefix tuning, resulting in sizable improvements to augmented data performance.

Summary Notes

Improving AI in Limited Data Settings: Data Augmentation and Efficient Tuning

In the evolving landscape of artificial intelligence (AI), the creation of large pretrained language models has been a breakthrough, offering remarkable accuracy in various tasks.

Yet, their demand for extensive resources presents challenges, especially where data or computational power is scarce. This post explores effective strategies to overcome these hurdles, emphasizing data augmentation and parameter-efficient tuning (PET).

Insights from researchers like Stephen Obadinma, Hong Yu Guo, and Xiaodan Zhu provide practical advice for AI engineers in enterprises.

Overcoming Data Shortages

Deep learning AI models require vast amounts of data. In many businesses, however, gathering this data is expensive, and privacy issues limit availability.

Additionally, large models such as BERT and RoBERTa are difficult to implement in environments with limited resources.

Solutions Through Data Augmentation and PET

Data augmentation (DA) and parameter-efficient tuning are promising approaches to tackle data scarcity.

DA methods like Easy Data Augmentation (EDA), Back Translation (BT), and Mixup create synthetic data, enhancing training dataset size and diversity.

PET techniques, including P-tuning v2 and LoRA, adjust a small portion of a model's parameters efficiently, making it easier to tailor large models to specific tasks.

Insights from Recent Studies

Data Augmentation Methods:

• EDA uses simple text changes to generate new training samples, including synonym replacement and sentence rearrangement.
• BT improves training data by translating text to another language and back again.
• Mixup produces synthetic data by combining multiple examples, helping the model to learn more general patterns.

Efficient Tuning Techniques:

• P-tuning v2 adds trainable prefix embeddings to each layer's input, minimally altering parameters while directing the model's focus.
• LoRA modifies the model by adjusting low-rank matrices, striking a balance between efficiency and flexibility.

Experiments with BERT and RoBERTa

Research by Obadinma and others tested BERT and RoBERTa models with various DA and PET combinations across five datasets. Key findings for AI engineers include:

• DA and PET Synergy: DA considerably boosts PET-tuned models' effectiveness, especially with limited data. The impact, however, varies by DA method and dataset.
• Selecting DA Methods: Not all DA techniques are equally beneficial; some might even harm performance, underscoring the importance of careful choice and experimentation.
• Compatibility with PET: Different PET methods respond uniquely to DA techniques, affecting optimization.

Main Points

Combining data augmentation with parameter-efficient tuning is a powerful strategy to address data scarcity and resource limitations. Choosing suitable DA methods and understanding PET characteristics can significantly improve model performance without extensive resources. However, it's essential to critically evaluate each combination to ensure effectiveness.

Looking Ahead and Ethical Considerations

Advancing AI in resource-constrained environments requires mindful consideration of ethics, particularly with data augmentation.

The risk of introducing bias or changing meaning calls for cautious use and further research into advanced augmentation strategies.

Conclusion

For enterprise AI engineers facing data and resource constraints, merging data augmentation with parameter-efficient tuning methods presents a viable solution.

By applying these techniques wisely, it's possible to boost model performance and adaptability, leading to more efficient and impactful AI applications.

Acknowledgements

This post is informed by the work of Stephen Obadinma, Hongyu Guo, and Xiaodan Zhu, whose research underlines the development of accessible AI technologies.

Their efforts, supported by significant fellowships and grants, are greatly appreciated.