With the emergence of large language models, such as LLaMA and OpenAI GPT-3,
In-Context Learning (ICL) gained significant attention due to its effectiveness
and efficiency. However, ICL is very sensitive to the choice, order, and
verbaliser used to encode the demonstrations in the prompt. Retrieval-Augmented
ICL methods try to address this problem by leveraging retrievers to extract
semantically related examples as demonstrations. While this approach yields
more accurate results, its robustness against various types of adversarial
attacks, including perturbations on test samples, demonstrations, and retrieved
data, remains under-explored. Our study reveals that retrieval-augmented models
can enhance robustness against test sample attacks, outperforming vanilla ICL
with a 4.87% reduction in Attack Success Rate (ASR); however, they exhibit
overconfidence in the demonstrations, leading to a 2% increase in ASR for
demonstration attacks. Adversarial training can help improve the robustness of
ICL methods to adversarial attacks; however, such a training scheme can be too
costly in the context of LLMs. As an alternative, we introduce an effective
training-free adversarial defence method, DARD, which enriches the example pool
with those attacked samples. We show that DARD yields improvements in
performance and robustness, achieving a 15% reduction in ASR over the
baselines. Code and data are released to encourage further research:
this https URL

通过引入检索增强联系学习方法，利用检索器提取语义相关示例作为演示，提高模型对测试样本的攻击稳健性，并引入一种无需训练的对抗防御方法 DARD，通过丰富示例池来改进模型性能和稳健性。