ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.

翻译：虽然在过去几个月里,已经对ChattGPT的各方面进行了评估,但公众仍不清楚它的稳健性,即对意外投入的性能。强健性在负责任的AI中尤其令人关切,特别是在安全关键应用方面。在本文件中,我们从对抗和分配(OOOD)的角度对ChatGPT的稳健性进行了彻底评估。为了做到这一点,我们采用AdvGLUE和ANLI基准来评估对抗性强力和Flipkart审查和DDXPlus医疗诊断数据集。我们选择了若干流行的基础模型作为基线。结果显示,ChatGPT在大多数对抗和OODD分类和翻译任务方面显示出一贯的优势。然而,绝对性业绩远非完美,这表明对抗性和ODDD稳健性仍然是对基础模型的重大威胁。此外,我们利用AdvGLUE和ANLI基准来评估OODD评估对抗性强性强性以及Flipkart审查和DXPLus医疗诊断数据集。我们选择了几种流行的基础模型作为基线。我们选择了几种基础。结果表明,即为深入研究提供非正式的答案,而不是最终的答案。</s>