In recent years, generative AI (GenAI), like large language models and
text-to-image models, has received significant attention across various
domains. However, ensuring the responsible generation of content by these
models is crucial for their real-world applicability. This raises an
interesting question: \textit{What should responsible GenAI generate, and what
should it not?} To answer the question, this paper investigates the practical
responsible requirements of both textual and visual generative models,
outlining five key considerations: generating truthful content, avoiding toxic
content, refusing harmful instruction, leaking no training data-related
content, and ensuring generated content identifiable. Specifically, we review
recent advancements and challenges in addressing these requirements. Besides,
we discuss and emphasize the importance of responsible GenAI across healthcare,
education, finance, and artificial general intelligence domains. Through a
unified perspective on both textual and visual generative models, this paper
aims to provide insights into practical safety-related issues and further
benefit the community in building responsible GenAI.

近年来，生成性人工智能（GenAI）如大型语言模型和文本到图像模型，在各个领域引起了重大关注。然而，确保这些模型生成的内容是负责任的对于它们在实际应用中的重要性至关重要。本文就文本和图像生成模型的实际负责任要求进行了调查研究，概述了五个关键考虑因素：生成真实内容、避免有害内容、拒绝有害指令、泄露与训练数据相关的内容以及确保生成的内容可识别。具体来说，我们回顾了解决这些要求的最新进展和挑战。此外，我们讨论并强调了在医疗保健、教育、金融和人工智能领域中负责任的 GenAI 的重要性。通过统一的文本和图像生成模型视角，本文旨在为实际安全相关问题提供见解，并进一步造福于建设负责任的 GenAI 了解相关社区。