$$\textit{GeoHard}$: Towards Measuring Class-wise Hardness through Modelling Class Semantics$

$\textit{GeoHard}$ : Towards Measuring Class-wise Hardness through Modelling Class Semantics

17 July 2024

Papers citing "$\textit{GeoHard}$: Towards Measuring Class-wise Hardness through Modelling Class Semantics"

7 / 7 papers shown

Title
On the Relation between Sensitivity and Accuracy in In-context Learning Yanda Chen Chen Zhao Zhou Yu Kathleen McKeown He He 180 77 0 16 Sep 2022
$Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information$ Understanding Dataset Difficulty with $\mathcal{V}$ -Usable Information Kawin Ethayarajh Yejin Choi Swabha Swayamdipta 154 157 0 16 Oct 2021
Fairness-aware Class Imbalanced Learning Shivashankar Subramanian Afshin Rahimi Timothy Baldwin Trevor Cohn Lea Frermann FaML 99 28 0 21 Sep 2021
Sensitivity as a Complexity Measure for Sequence Classification Tasks Michael Hahn Dan Jurafsky Richard Futrell 135 22 0 21 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 238 1,898 0 31 Dec 2020
DynaSent: A Dynamic Benchmark for Sentiment Analysis Christopher Potts Zhengxuan Wu Atticus Geiger Douwe Kiela 217 76 0 30 Dec 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 6,927 0 20 Apr 2018