How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluation

10 June 2021

Papers citing "How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluation"

9 / 9 papers shown

Title
End-User Development for Artificial Intelligence: A Systematic Literature Review Andrea Esposito Miriana Calvano Antonio Curci Giuseppe Desolda R. Lanzilotti Claudia Lorusso Antonio Piccinno 17 5 0 14 Apr 2023
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity Anjana Arunkumar Shubham Sharma Rakhi Agrawal Sriramakrishnan Chandrasekaran Chris Bryan 34 0 0 12 Apr 2023
RealTime QA: What's the Answer Right Now? Jungo Kasai Keisuke Sakaguchi Yoichi Takahashi Ronan Le Bras Akari Asai Xinyan Velocity Yu Dragomir R. Radev Noah A. Smith Yejin Choi Kentaro Inui KELM 45 165 0 27 Jul 2022
Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions Mihir Parmar Swaroop Mishra Mor Geva Chitta Baral 30 55 0 01 May 2022
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness Tejas Gokhale Swaroop Mishra Man Luo Bhavdeep Singh Sachdeva Chitta Baral 52 29 0 15 Mar 2022
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines Jiachen Sun Akshay Mehra B. Kailkhura Pin-Yu Chen Dan Hendrycks Jihun Hamm Z. Morley Mao AAML 33 21 0 01 Dec 2021
The Benchmark Lottery Mostafa Dehghani Yi Tay A. Gritsenko Zhe Zhao N. Houlsby Fernando Diaz Donald Metzler Oriol Vinyals 42 89 0 14 Jul 2021
Certified Robustness to Adversarial Word Substitutions Robin Jia Aditi Raghunathan Kerem Göksel Percy Liang AAML 183 291 0 03 Sep 2019
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 205 711 0 17 Apr 2018