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.

翻译：近年来，生成式AI（GenAI），如大型语言模型和文本到图像模型，在各个领域都受到了广泛关注。然而，确保这些模型负责任地生成内容，对于其现实世界中的适用性至关重要。这引发了一个有趣的问题：负责任的GenAI应该生成什么，又不该生成什么？为了回答这个问题，本文研究了文本和视觉生成模型在实际中需承担的负责任要求，概述了五个关键考量：生成真实内容、避免有毒内容、拒绝有害指令、不泄露与训练数据相关的内容，以及确保生成内容可识别。具体而言，我们回顾了应对这些要求的最新进展和挑战。此外，我们讨论并强调了负责任的GenAI在医疗、教育、金融和通用人工智能领域的重要性。通过从文本和视觉生成模型的统一视角出发，本文旨在为实际安全问题提供见解，并进一步助力学术界构建负责任的GenAI。