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The vulnerability of PLMs under backdoor attacks has been
proved with increasing evidence in the literature. In this paper, we present several
simple yet effective training strategies that could effectively defend against such
attacks.
poisoned_test_file: the path of the poisoned testing data
poisoned_train_file: the path of the poisoned training data
clean_test_file: the path of the clean testing data
mid_dim: the bottleneck dimension of the reparameterization network
lora_r: the LoRA rank r
(2) Some arguments in the Adapter config file:
poisoned_test_file: the path of the poisoned testing data
poisoned_train_file: the path of the poisoned training data
clean_test_file: the path of the clean testing data
mid_dim: the bottleneck dimension of the reparameterization network
bottleneck_dim: the projection dimension of the Adapter
(3) Some arguments in the Prefix-Tuning config file:
poisoned_test_file: the path of the poisoned testing data
poisoned_train_file: the path of the poisoned training data
clean_test_file: the path of the clean testing data
mid_dim: the bottleneck dimension of the reparameterization network
prefix_token_num: the number of prefix tokens
Run the experiments
(1) To defend against word-level attack on SST-2 with low-rank reparameterized LoRA, run the following code:
cd ./examples/examples_text-classification
bash run_poison.sh 6 6 lora_roberta-base-sst2-badnet-5
(2) To defend against word-level attack on SST-2 with low-rank reparameterized Adapter, run the following code:
cd ./examples/examples_text-classification
bash run_poison.sh 6 6 adapter_roberta-base-sst2-badnet-5
(3) To defend against word-level attack on SST-2 with low-rank reparameterized Prefix-Tuning, run the following code:
cd ./examples/examples_text-classification
bash run_poison.sh 6 6 prefix_roberta-base-sst2-badnet-5
