Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis
This work highlights a critical limitation for financial practitioners using lightweight tuning methods, indicating it is incremental as it tests an existing method on new conditions.
The paper investigates the robustness of prefix tuning compared to fine-tuning on noisy financial sentiment analysis data, finding that fine-tuning is more robust with prefix tuning showing significant performance drops and high variance in F1 scores as noise increases.
The invention of transformer-based models such as BERT, GPT, and RoBERTa has enabled researchers and financial companies to finetune these powerful models and use them in different downstream tasks to achieve state-of-the-art performance. Recently, a lightweight alternative (approximately 0.1% - 3% of the original model parameters) to fine-tuning, known as prefix tuning has been introduced. This method freezes the model parameters and only updates the prefix to achieve performance comparable to full fine-tuning. Prefix tuning enables researchers and financial practitioners to achieve similar results with much fewer parameters. In this paper, we explore the robustness of prefix tuning when facing noisy data. Our experiments demonstrate that fine-tuning is more robust to noise than prefix tuning -- the latter method faces a significant decrease in performance on most corrupted data sets with increasing noise levels. Furthermore, prefix tuning has high variances in the F1 scores compared to fine-tuning in many corruption methods. We strongly advocate that caution should be carefully taken when applying the state-of-the-art prefix tuning method to noisy data.