Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis

TL;DR

This paper investigates the robustness of prefix tuning versus fine-tuning in transformer models for financial sentiment analysis, revealing that prefix tuning is less robust to noisy data and exhibits higher variance in performance.

Contribution

The study provides the first systematic analysis of prefix tuning's robustness to noise in financial NLP tasks, highlighting its limitations compared to fine-tuning.

Findings

Fine-tuning is more robust to noise than prefix tuning.

Prefix tuning shows high variance in F1 scores across different corruptions.

Performance of prefix tuning significantly decreases with increasing noise levels.

Abstract

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.