Better Fine-Tuning by Reducing Representational Collapse

TL;DR

This paper introduces a simplified trust region-based fine-tuning method for pre-trained language models that reduces representational collapse, improves stability, and enhances performance across various NLP tasks.

Contribution

A new efficient fine-tuning approach using parametric noise within trust region theory, reducing representational collapse and outperforming previous methods.

Findings

Matches or exceeds previous trust region methods in performance

Faster fine-tuning process

Less prone to representational collapse, maintaining generalizable representations

Abstract

Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance. We also introduce a new analysis to motivate the use of trust region methods more generally, by studying representational collapse; the degradation of generalizable representations from pre-trained models as they are fine-tuned for a specific end task. Extensive experiments show that our fine-tuning method matches or exceeds the performance of previous trust region methods on a range of understanding and generation tasks (including DailyMail/CNN, Gigaword, Reddit TIFU, and the GLUE benchmark), while also being much faster. We also show that it is less prone to representation collapse; the pre-trained models maintain more generalizable representations every time they are fine-tuned.