- Title
- Position: TRUSTLLM: Trustworthiness in Large Language Models
- Creators
- Yue Huang - University of Notre DameLichao Sun - Lehigh UniversityHaoran Wang - Illinois Institute of TechnologySiyuan Wu - CISPA, GermanyQihui Zhang - CISPA, GermanyYuan Li - University of CambridgeChujie Gao - CISPA, GermanyYixin Huang - Institut Polytechnique de ParisWenhan Lyu - William & MaryYixuan Zhang - William & MaryXiner Li - Texas A&M University, United StatesHanchi Sun - Lehigh UniversityZhengliang Liu - University of GeorgiaYixin Liu - Lehigh UniversityYijue Wang - Samsung Research America, United StatesZhikun Zhang - Stanford UniversityBertie Vidgen - University of OxfordBhavya Kailkhura - Lawrence Livermore National LaboratoryCaiming Xiong - Salesforce Research, United StatesChaowei Xiao - University of Wisconsin–MadisonChunyuan Li - Microsoft Research, United StatesEric Xing - Mohamed bin Zayed University of Artificial IntelligenceFurong Huang - University of Maryland, United StatesHao Liu - University of California, BerkeleyHeng Ji - University of Illinois Urbana-ChampaignHongyi Wang - Rutgers, The State University of New JerseyHuan Zhang - University of Illinois Urbana-ChampaignHuaxiu Yao - University of North Carolina at Chapel HillManolis Kellis - Massachusetts Institute of TechnologyMarinka Zitnik - Harvard UniversityMeng Jiang - University of Notre DameMohit Bansal - University of North Carolina at Chapel HillJames Zou - Stanford UniversityJian Pei - Duke UniversityJian Liu - University of Tennessee at KnoxvilleJianfeng Gao - Microsoft Research, United StatesJiawei Han - University of Illinois Urbana-ChampaignJieyu Zhao - University of Southern California, United StatesJiliang Tang - Michigan State UniversityJindong Wang - Microsoft Research Asia, ChinaJoaquin Vanschoren - Eindhoven University of TechnologyJohn C. Mitchell - Stanford UniversityKai Shu - Illinois Institute of TechnologyKaidi Xu - Drexel University, United StatesKai Wei Chang - University of California, Los AngelesLifang He - Lehigh UniversityLifu Huang - Virginia TechMichael Backes - CISPA, GermanyNeil Zhenqiang Gong - Duke UniversityPhilip S. Yu - University of Illinois ChicagoPin Yu Chen - IBM Research, United StatesQuanquan Gu - University of California, Los AngelesRan Xu - Salesforce Research, United StatesRex Ying - Yale UniversityShuiwang Ji - Texas A&M University, United StatesSuman Jana - Columbia UniversityTianlong Chen - University of North Carolina at Chapel HillTianming Liu - University of GeorgiaTianyi Zhou - University of Maryland, United StatesWilliam Wang - University of California, Santa BarbaraXiang Li - Massachusetts General HospitalXiangliang Zhang - University of Notre DameXiao Wang - Northwestern University, United StatesXing Xie - Microsoft Research Asia, ChinaXun Chen - Samsung Research America, United StatesXuyu Wang - Florida International University, United StatesYan Liu - University of Southern California, United StatesYanfang Ye - University of Notre DameYinzhi Cao - Johns Hopkins UniversityYong Chen - University of PennsylvaniaYue Zhao - University of Southern California, United States
- Publication Details
- International Conference on Machine Learning, ICML 2024, v 235, pp 20166-20270
- Publisher
- Proceedings of Machine Learning Research
- Grant note
- Lawrence Livermore National Laboratory (http://data.elsevier.com/vocabulary/SciValFunders/100006227) CRII-2246067; FRGS00011497 / National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001) Microsoft Accelerate Foundation FRGS00011497 / National Natural Science Foundation of China (501100001809) University of Maryland (100008510) U.S. Department of Energy (http://data.elsevier.com/vocabulary/SciValFunders/100000015) University of Maryland (http://data.elsevier.com/vocabulary/SciValFunders/100008510) DE-AC52-07NA27344 / Lawrence Livermore National Laboratory (http://data.elsevier.com/vocabulary/SciValFunders/100006227) National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001) Microsoft Research (100006112)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-85203837986
- Other Identifier
- 991022202094304721
Conference proceeding
Position: TRUSTLLM: Trustworthiness in Large Language Models
International Conference on Machine Learning, ICML 2024, v 235, pp 20166-20270
2024
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