RoCoIns: Enhancing Robustness of Large Language Models through
  Code-Style Instructions
Yuansen Zhang Xiao Wang Zhiheng Xi Han Xia Tao Gui Qi Zhang Xuanjing Huang
Large Language Models (LLMs) have showcased remarkable capabilities in
following human instructions. However, recent studies have raised concerns
about the robustness of LLMs when prompted with instructions combining textual
adversarial samples. In this paper, drawing inspiration from recent works that
LLMs are sensitive to the design of the instructions, we utilize instructions
in code style, which are more structural and less ambiguous, to replace
typically natural language instructions. Through this conversion, we provide
LLMs with more precise instructions and strengthen the robustness of LLMs.
Moreover, under few-shot scenarios, we propose a novel method to compose
in-context demonstrations using both clean and adversarial samples
(\textit{adversarial context method}) to further boost the robustness of the
LLMs. Experiments on eight robustness datasets show that our method
consistently outperforms prompting LLMs with natural language instructions. For
example, with gpt-3.5-turbo, our method achieves an improvement of 5.68\% in
test set accuracy and a reduction of 5.66 points in Attack Success Rate (ASR).