Journal article
Reliable and Responsible Foundation Models
Transactions on Machine Learning Research, v 2025
2025
Abstract
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.
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Details
- Title
- Reliable and Responsible Foundation Models
- Creators
- Xinyu Yang - Carnegie Mellon UniversityJunlin Han - University of OxfordRishi Bommasani - Stanford UniversityJinqi Luo - University of PennsylvaniaWenjie Qu - National University of Singapore, SingaporeWangchunshu Zhou - ETH ZurichAdel Bibi - University of OxfordXiyao Wang - University of Maryland, United StatesJaehong Yoon - University of North Carolina at Chapel HillElias Stengel-Eskin - University of North Carolina at Chapel HillShengbang Tong - New York University, United StatesLingfeng Shen - Johns Hopkins UniversityRafael Rafailov - Stanford UniversityRunjia Li - University of OxfordZhaoyang Wang - University of North Carolina at Chapel HillYiyang Zhou - University of North Carolina at Chapel HillChenhang Cui - National University of Singapore, SingaporeYu Wang - University of California, San Diego, United StatesWenhao Zheng - University of North Carolina at Chapel HillHuichi Zhou - Imperial College LondonJindong Gu - University of OxfordZhaorun Chen - University of ChicagoPeng Xia - University of North Carolina at Chapel HillTony Lee - Stanford UniversityThomas Zollo - Columbia UniversityVikash Sehwag - Princeton UniversityJixuan Leng - Carnegie Mellon UniversityJiuhai Chen - University of Maryland, United StatesYuxin Wen - University of PennsylvaniaHuan Zhang - Mila - Quebec Artificial Intelligence InstituteZhun Deng - Imperial College LondonLinjun Zhang - Rutgers, The State University of New JerseyPavel Izmailov - New York University, United StatesPang Wei Koh - University of WashingtonYulia Tsvetkov - University of WashingtonAndrew Wilson - New York University, United StatesJiaheng Zhang - National University of Singapore, SingaporeJames Zou - Stanford UniversityCihang Xie - University of California, Santa CruzHao Wang - Rutgers, The State University of New JerseyPhilip Torr - University of OxfordJulian McAuley - University of California, San Diego, United StatesDavid Alvarez-Melis - Harvard UniversityFlorian Tramèr - ETH ZurichKaidi Xu - Drexel University, United StatesSuman Jana - Imperial College LondonChris Callison-Burch - University of PennsylvaniaRene Vidal - University of PennsylvaniaFilippos Kokkinos - University College London, United KingdomMohit Bansal - University of North Carolina at Chapel HillBeidi Chen - Carnegie Mellon UniversityHuaxiu Yao - University of North Carolina at Chapel Hill
- Publication Details
- Transactions on Machine Learning Research, v 2025
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-105025642630
- Other Identifier
- 991022148042804721