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Adversarially Constructed Evaluation Sets Are More Challenging, but May Not Be Fair
16 November 2021
Jason Phang
Angelica Chen
William Huang
Samuel R. Bowman
AAML
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Papers citing
"Adversarially Constructed Evaluation Sets Are More Challenging, but May Not Be Fair"
6 / 6 papers shown
Title
Improving Model Evaluation using SMART Filtering of Benchmark Datasets
Vipul Gupta
Candace Ross
David Pantoja
R. Passonneau
Megan Ung
Adina Williams
52
1
0
26 Oct 2024
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
Alisa Liu
Swabha Swayamdipta
Noah A. Smith
Yejin Choi
30
212
0
16 Jan 2022
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
Max Bartolo
Tristan Thrush
Sebastian Riedel
Pontus Stenetorp
Robin Jia
Douwe Kiela
15
33
0
16 Dec 2021
Analyzing Dynamic Adversarial Training Data in the Limit
Eric Wallace
Adina Williams
Robin Jia
Douwe Kiela
184
29
0
16 Oct 2021
ANLIzing the Adversarial Natural Language Inference Dataset
Adina Williams
Tristan Thrush
Douwe Kiela
AAML
166
45
0
24 Oct 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Jinpeng Wang
Amanpreet Singh
Julian Michael
Felix Hill
Omer Levy
Samuel R. Bowman
ELM
294
6,943
0
20 Apr 2018
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