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：ChatGPT是由OpenAI发布的最新聊天机器人服务，近几个月来一直受到越来越多的关注。虽然已经对ChatGPT的各个方面进行了评估，但是它的稳健性，即应对意料之外的输入表现出的性能，对公众来说仍然不清楚。在负责任的人工智能应用中，稳健性尤为重要，特别是对于安全关键应用。在本文中，我们从对抗性和超分布 (OOD) 的视角对ChatGPT的稳健性进行了全面的评估。为此，我们使用AdvGLUE和ANLI基准来评估对抗稳健性；使用Flipkart评论和DDXPlus医疗诊断数据集进行OOD评估。我们选择了几个常见基础模型作为基线。结果表明，ChatGPT在大多数对抗性和OOD分类和翻译任务上都显示出了一致的优势。然而，绝对性能远非完美，这表明对抗性和OOD稳健性仍然是基础模型的重大威胁。此外，ChatGPT在理解与对话相关的文本方面表现出惊人的性能，我们发现它往往会为医疗任务提供非正式的建议而非明确的答案。最后，我们提出了可能的研究方向的深入讨论。