Model-tuning Via Prompts Makes NLP Models Adversarially Robust

Mrigank Raman, Pratyush Maini, J Zico Kolter, Zachary Chase Lipton, Danish Pruthi

Keywords: Natural Language Processing, Language Models, BERT, RoBERTa, Prompting, Adversarial Robustness

TL;DR: We make the observation that tuning NLP models via prompting provides robustness against adversarial attacks and beats state-of-art methods employing adversarial training. We explain the reasons for these gains.

Abstract:

In recent years, NLP practitioners have converged on the following practice:
(i) import an off-the-shelf pretrained (masked) language model;
(ii) append a multilayer perceptron atop the CLS token's hidden representation
(with randomly initialized weights);
and (iii) fine-tune the entire model on a downstream task (MLP-FT).
This
procedure
has
produced massive gains
on standard NLP benchmarks,
but these models remain brittle, even to
mild adversarial perturbations,
such as word-level synonym substitutions.
In this work, we demonstrate surprising gains
in adversarial robustness enjoyed by
Model-tuning Via Prompts (MVP),
an alternative method of adapting to downstream tasks.
Rather than modifying the model (by appending an MLP head),
MVP instead modifies the input (by appending a prompt template).
Across three classification datasets,
MVP improves performance against adversarial word-level synonym substitutions by an average of 8%
over standard methods and even outperforms
adversarial training-based state-of-art defenses by 3.5%.
By combining MVP with adversarial training,
we achieve further improvements in robust accuracy
while maintaining clean accuracy.
Finally, we conduct ablations to investigate
the mechanism underlying these gains.
Notably, we find that the main causes of vulnerability of MLP-FT
can be attributed to the misalignment between pre-training and fine-tuning tasks,
and the randomly initialized MLP parameters.

Submission Number: 40