  Pre-trained language models (PLMs) have attracted enormous attention over the
past few years with their unparalleled performances. Meanwhile, the soaring
cost to train PLMs as well as their amazing generalizability have jointly
contributed to few-shot fine-tuning and prompting as the most popular training
paradigms for natural language processing (NLP) models. Nevertheless, existing
studies have shown that these NLP models can be backdoored such that model
behavior is manipulated when trigger tokens are presented. In this paper, we
propose PromptFix, a novel backdoor mitigation strategy for NLP models via
adversarial prompt-tuning in few-shot settings. Unlike existing NLP backdoor
removal methods, which rely on accurate trigger inversion and subsequent model
fine-tuning, PromptFix keeps the model parameters intact and only utilizes two
extra sets of soft tokens which approximate the trigger and counteract it
respectively. The use of soft tokens and adversarial optimization eliminates
the need to enumerate possible backdoor configurations and enables an adaptive
balance between trigger finding and preservation of performance. Experiments
with various backdoor attacks validate the effectiveness of the proposed method
and the performances when domain shift is present further shows PromptFix's
applicability to models pretrained on unknown data source which is the common
case in prompt tuning scenarios.
