703 research outputs found
BadPrompt: Backdoor Attacks on Continuous Prompts
The prompt-based learning paradigm has gained much research attention
recently. It has achieved state-of-the-art performance on several NLP tasks,
especially in the few-shot scenarios. While steering the downstream tasks, few
works have been reported to investigate the security problems of the
prompt-based models. In this paper, we conduct the first study on the
vulnerability of the continuous prompt learning algorithm to backdoor attacks.
We observe that the few-shot scenarios have posed a great challenge to backdoor
attacks on the prompt-based models, limiting the usability of existing NLP
backdoor methods. To address this challenge, we propose BadPrompt, a
lightweight and task-adaptive algorithm, to backdoor attack continuous prompts.
Specially, BadPrompt first generates candidate triggers which are indicative
for predicting the targeted label and dissimilar to the samples of the
non-targeted labels. Then, it automatically selects the most effective and
invisible trigger for each sample with an adaptive trigger optimization
algorithm. We evaluate the performance of BadPrompt on five datasets and two
continuous prompt models. The results exhibit the abilities of BadPrompt to
effectively attack continuous prompts while maintaining high performance on the
clean test sets, outperforming the baseline models by a large margin. The
source code of BadPrompt is publicly available at
https://github.com/papersPapers/BadPrompt.Comment: Accepted at NeurIPS 202
Quantum hypothesis testing via robust quantum control
Quantum hypothesis testing plays a pivotal role in quantum technologies,
making decisions or drawing conclusions about quantum systems based on observed
data. Recently, quantum control techniques have been successfully applied to
quantum hypothesis testing, enabling the reduction of error probabilities in
the task of distinguishing magnetic fields in presence of environmental noise.
In real-world physical systems, such control is prone to various channels of
inaccuracies. Therefore improving the robustness of quantum control in the
context of quantum hypothesis testing is crucial. In this work, we utilize
optimal control methods to compare scenarios with and without accounting for
the effects of signal frequency inaccuracies. For parallel dephasing and
spontaneous emission, the optimal control inherently demonstrates a certain
level of robustness, while in the case of transverse dephasing with an
imperfect signal, it may result in a higher error probability compared to the
uncontrolled scheme. To overcome these limitations, we introduce a robust
control approach optimized for a range of signal noise, demonstrating superior
robustness beyond the predefined tolerance window. On average, both the optimal
control and robust control show improvements over the uncontrolled schemes for
various dephasing or decay rates, with the robust control yielding the lowest
error probability.Comment: 20 pages, 6 figure
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