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How do Decisions Emerge across Layers in Neural Models? Interpretation
  with Differentiable Masking

How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking

30 April 2020
Nicola De Cao
M. Schlichtkrull
Wilker Aziz
Ivan Titov
ArXivPDFHTML

Papers citing "How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking"

25 / 25 papers shown
Title
MIB: A Mechanistic Interpretability Benchmark
MIB: A Mechanistic Interpretability Benchmark
Aaron Mueller
Atticus Geiger
Sarah Wiegreffe
Dana Arad
Iván Arcuschin
...
Alessandro Stolfo
Martin Tutek
Amir Zur
David Bau
Yonatan Belinkov
51
1
0
17 Apr 2025
Where does In-context Translation Happen in Large Language Models
Where does In-context Translation Happen in Large Language Models
Suzanna Sia
David Mueller
Kevin Duh
LRM
41
0
0
07 Mar 2024
Interpreting Sentiment Composition with Latent Semantic Tree
Interpreting Sentiment Composition with Latent Semantic Tree
Zhongtao Jiang
Yuanzhe Zhang
Cao Liu
Jiansong Chen
Jun Zhao
Kang Liu
CoGe
29
0
0
31 Aug 2023
Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop
  Fact Verification
Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification
Jiasheng Si
Yingjie Zhu
Deyu Zhou
AAML
52
3
0
16 May 2023
VISION DIFFMASK: Faithful Interpretation of Vision Transformers with
  Differentiable Patch Masking
VISION DIFFMASK: Faithful Interpretation of Vision Transformers with Differentiable Patch Masking
A. Nalmpantis
Apostolos Panagiotopoulos
John Gkountouras
Konstantinos Papakostas
Wilker Aziz
15
4
0
13 Apr 2023
Automated multilingual detection of Pro-Kremlin propaganda in newspapers
  and Telegram posts
Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts
Veronika Solopova
Oana-Iuliana Popescu
Christoph Benzmüller
Tim Landgraf
13
22
0
25 Jan 2023
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine
  Reading Comprehension
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension
Suzhe He
Shumin Shi
Chenghao Wu
31
0
0
06 Oct 2022
Differentiable Mathematical Programming for Object-Centric
  Representation Learning
Differentiable Mathematical Programming for Object-Centric Representation Learning
Adeel Pervez
Phillip Lippe
E. Gavves
OCL
44
5
0
05 Oct 2022
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP
Lijie Wang
Yaozong Shen
Shu-ping Peng
Shuai Zhang
Xinyan Xiao
Hao Liu
Hongxuan Tang
Ying Chen
Hua-Hong Wu
Haifeng Wang
ELM
19
21
0
23 May 2022
Sparse Interventions in Language Models with Differentiable Masking
Sparse Interventions in Language Models with Differentiable Masking
Nicola De Cao
Leon Schmid
Dieuwke Hupkes
Ivan Titov
40
27
0
13 Dec 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
17
44
0
20 Oct 2021
How Does Adversarial Fine-Tuning Benefit BERT?
How Does Adversarial Fine-Tuning Benefit BERT?
J. Ebrahimi
Hao Yang
Wei Zhang
AAML
26
4
0
31 Aug 2021
Neuron-level Interpretation of Deep NLP Models: A Survey
Neuron-level Interpretation of Deep NLP Models: A Survey
Hassan Sajjad
Nadir Durrani
Fahim Dalvi
MILM
AI4CE
35
80
0
30 Aug 2021
Translation Error Detection as Rationale Extraction
Translation Error Detection as Rationale Extraction
M. Fomicheva
Lucia Specia
Nikolaos Aletras
21
23
0
27 Aug 2021
The Out-of-Distribution Problem in Explainability and Search Methods for
  Feature Importance Explanations
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
Peter Hase
Harry Xie
Joey Tianyi Zhou
OODD
LRM
FAtt
18
91
0
01 Jun 2021
Editing Factual Knowledge in Language Models
Editing Factual Knowledge in Language Models
Nicola De Cao
Wilker Aziz
Ivan Titov
KELM
50
474
0
16 Apr 2021
When Can Models Learn From Explanations? A Formal Framework for
  Understanding the Roles of Explanation Data
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data
Peter Hase
Joey Tianyi Zhou
XAI
25
87
0
03 Feb 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
Modifying Memories in Transformer Models
Modifying Memories in Transformer Models
Chen Zhu
A. S. Rawat
Manzil Zaheer
Srinadh Bhojanapalli
Daliang Li
Felix X. Yu
Sanjiv Kumar
KELM
23
192
0
01 Dec 2020
The elephant in the interpretability room: Why use attention as
  explanation when we have saliency methods?
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
Jasmijn Bastings
Katja Filippova
XAI
LRM
37
172
0
12 Oct 2020
Learning Variational Word Masks to Improve the Interpretability of
  Neural Text Classifiers
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers
Hanjie Chen
Yangfeng Ji
AAML
VLM
13
63
0
01 Oct 2020
Interpreting Graph Neural Networks for NLP With Differentiable Edge
  Masking
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
M. Schlichtkrull
Nicola De Cao
Ivan Titov
AI4CE
31
214
0
01 Oct 2020
The Bottom-up Evolution of Representations in the Transformer: A Study
  with Machine Translation and Language Modeling Objectives
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Elena Voita
Rico Sennrich
Ivan Titov
198
181
0
03 Sep 2019
Improving fairness in machine learning systems: What do industry
  practitioners need?
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein
Jennifer Wortman Vaughan
Hal Daumé
Miroslav Dudík
Hanna M. Wallach
FaML
HAI
192
742
0
13 Dec 2018
e-SNLI: Natural Language Inference with Natural Language Explanations
e-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu
Tim Rocktaschel
Thomas Lukasiewicz
Phil Blunsom
LRM
260
620
0
04 Dec 2018
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