Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), and Video Generative Models, have become essential tools with broad applications across various domains such as law, medicine, education, finance, science, and beyond. As these models see increasing real-world deployment, ensuring their reliability and responsibility has become critical for academia, industry, and government. This survey addresses the reliable and responsible development of foundation models. We explore critical issues, including bias and fairness, security and privacy, uncertainty, explainability, and distribution shift. Our research also covers model limitations, such as hallucinations, as well as methods like alignment and Artificial Intelligence-Generated Content (AIGC) detection. For each area, we review the current state of the field and outline concrete future research directions. Additionally, we discuss the intersections between these areas, highlighting their connections and shared challenges. We hope our survey fosters the development of foundation models that are not only powerful but also ethical, trustworthy, reliable, and socially responsible.

翻译：基础模型，包括大型语言模型（LLMs）、多模态大型语言模型（MLLMs）、图像生成模型（即文本到图像模型和图像编辑模型）以及视频生成模型，已成为在法学、医学、教育、金融、科学等众多领域广泛应用的关键工具。随着这些模型在现实世界中的部署日益增多，确保其可靠性与负责任性，对学术界、工业界和政府而言变得至关重要。本综述探讨基础模型的可靠与负责任发展。我们研究了关键问题，包括偏见与公平性、安全与隐私、不确定性、可解释性以及分布偏移。我们的研究还涵盖了模型局限性，例如幻觉，以及对齐和人工智能生成内容（AIGC）检测等方法。针对每个领域，我们回顾了该领域的现状，并概述了具体的未来研究方向。此外，我们讨论了这些领域之间的交叉点，强调了它们之间的联系与共同挑战。我们希望本综述能促进基础模型的发展，使其不仅强大，而且合乎伦理、值得信赖、可靠并具有社会责任感。