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Pair the Dots: Jointly Examining Training History and Test Stimuli for
  Model Interpretability

Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability

14 October 2020
Yuxian Meng
Chun Fan
Zijun Sun
Eduard H. Hovy
Fei Wu
Jiwei Li
    FAtt
ArXivPDFHTML

Papers citing "Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability"

11 / 11 papers shown
Title
How do languages influence each other? Studying cross-lingual data
  sharing during LM fine-tuning
How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning
Rochelle Choenni
Dan Garrette
Ekaterina Shutova
40
16
0
22 May 2023
A General Framework for Defending Against Backdoor Attacks via Influence
  Graph
A General Framework for Defending Against Backdoor Attacks via Influence Graph
Xiaofei Sun
Jiwei Li
Xiaoya Li
Ziyao Wang
Tianwei Zhang
Han Qiu
Fei Wu
Chun Fan
AAML
TDI
24
5
0
29 Nov 2021
Interpreting Deep Learning Models in Natural Language Processing: A
  Review
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun
Diyi Yang
Xiaoya Li
Tianwei Zhang
Yuxian Meng
Han Qiu
Guoyin Wang
Eduard H. Hovy
Jiwei Li
19
44
0
20 Oct 2021
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness,
  and Semantic Evaluation
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation
Wei Zhang
Ziming Huang
Yada Zhu
Guangnan Ye
Xiaodong Cui
Fan Zhang
31
17
0
09 Jun 2021
FastIF: Scalable Influence Functions for Efficient Model Interpretation
  and Debugging
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
Han Guo
Nazneen Rajani
Peter Hase
Joey Tianyi Zhou
Caiming Xiong
TDI
41
102
0
31 Dec 2020
Self-Explaining Structures Improve NLP Models
Self-Explaining Structures Improve NLP Models
Zijun Sun
Chun Fan
Qinghong Han
Xiaofei Sun
Yuxian Meng
Fei Wu
Jiwei Li
MILM
XAI
LRM
FAtt
46
38
0
03 Dec 2020
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
Chen Zhu
Yu Cheng
Zhe Gan
S. Sun
Tom Goldstein
Jingjing Liu
AAML
232
438
0
25 Sep 2019
Certified Robustness to Adversarial Word Substitutions
Certified Robustness to Adversarial Word Substitutions
Robin Jia
Aditi Raghunathan
Kerem Göksel
Percy Liang
AAML
188
291
0
03 Sep 2019
Generating Natural Language Adversarial Examples
Generating Natural Language Adversarial Examples
M. Alzantot
Yash Sharma
Ahmed Elgohary
Bo-Jhang Ho
Mani B. Srivastava
Kai-Wei Chang
AAML
258
915
0
21 Apr 2018
A causal framework for explaining the predictions of black-box
  sequence-to-sequence models
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis
Tommi Jaakkola
CML
232
200
0
06 Jul 2017
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
293
5,842
0
08 Jul 2016
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