Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models

24 May 2021

Papers citing "Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models"

7 / 7 papers shown

Title
No Query, No Access W. Wang Siyuan Liang Y. Zhang X. Jia Hao Lin Xiaochun Cao AAML 21 0 0 12 May 2025
IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method Mihyeon Kim Juhyoung Park Youngbin Kim 31 0 0 11 May 2025
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing Jiabao Ji Bairu Hou Zhen Zhang Guanhua Zhang Wenqi Fan Qing Li Yang Zhang Gaowen Liu Sijia Liu Shiyu Chang AAML 30 5 0 18 Apr 2024
EMBRACE: Evaluation and Modifications for Boosting RACE M. Zyrianova Dmytro Kalpakchi Johan Boye 19 1 0 15 May 2023
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification Lingfeng Shen Ze Zhang Haiyun Jiang Ying Chen AAML 37 5 0 03 Feb 2023
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models Lan Jiang Hao Zhou Yankai Lin Peng Li Jie Zhou R. Jiang AAML 27 8 0 18 Oct 2022
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents Yicheng Zou Hongwei Liu Tao Gui Junzhe Wang Qi Zhang M. Tang Haixiang Li Dan Wang DRL 35 29 0 06 Mar 2022