Reliable and Responsible Foundation Models

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, 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.

Authors

• Xinyu Yang Nankai University
• Junlin Han
• Rishi Bommasani
• Jinqi Luo
• Wenjie Qu National University of Singapore
• Wangchunshu Zhou
• Adel Bibi University of Oxford
• Xiyao Wang
• Jaehong Yoon Nanyang Technological University
• Elias Stengel-Eskin
• Shengbang Tong New York University
• Lingfeng Shen
• Rafael Rafailov
• Runjia Li
• Zhaoyang Wang Ministry of Education, Key Laboratory of Collaborative Intelligence Systems, Xidian University DAMO Academy, Alibaba Group, 310023, Hangzhou, China
• Yiyang Zhou
• Chenhang Cui University of Electronic Science and Technology of China
• Yu Wang
• Wenhao Zheng
• Huichi Zhou Department of Bioengineering, Imperial College London, UK
• Jindong Gu University of Oxford, Oxford, England
• Zhaorun Chen
• Peng Xia UNC-Chapel Hill
• Tony Lee
• Thomas P Zollo
• Vikash Sehwag
• Jixuan Leng
• Jiuhai Chen
• Yuxin Wen
• Huan ZHang China Mobile Information Technology Center
• Zhun Deng
• Linjun Zhang
• Pavel Izmailov
• Pang Wei Koh
• Yulia Tsvetkov
• Andrew Gordon Wilson
• Jiaheng Zhang
• James Zou Stanford University
• Cihang Xie University of California, Santa Cruz
• Hao Wang Monash University
• Philip Torr
• Julian McAuley UCSD
• David Alvarez-Melis
• Florian Tramèr
• Kaidi Xu Drexel University
• Suman Jana
• Chris Callison-Burch University of Pennsylvania
• Rene Vidal
• Filippos Kokkinos
• Mohit Bansal University of North Carolina, Chapel Hill
• Beidi Chen
• Huaxiu Yao University of North Carolina at Chapel Hill

Keywords

No keywords are indexed for this paper.