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Is Sparse Attention more Interpretable?

Is Sparse Attention more Interpretable?

2 June 2021
Clara Meister
Stefan Lazov
Isabelle Augenstein
Ryan Cotterell
    MILM
ArXivPDFHTML

Papers citing "Is Sparse Attention more Interpretable?"

12 / 12 papers shown
Title
Hierarchical Attention Network for Interpretable ECG-based Heart Disease Classification
Hierarchical Attention Network for Interpretable ECG-based Heart Disease Classification
Mario Padilla Rodriguez
Mohamed Nafea
28
0
0
25 Mar 2025
DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion
DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion
Huiguo He
Huan Yang
Zixi Tuo
Yuan Zhou
Qiuyue Wang
Yuhang Zhang
Zeyu Liu
Wenhao Huang
Hongyang Chao
Jian Yin
DiffM
VGen
62
12
0
17 Jul 2024
Sparse Autoencoders Find Highly Interpretable Features in Language
  Models
Sparse Autoencoders Find Highly Interpretable Features in Language Models
Hoagy Cunningham
Aidan Ewart
Logan Riggs
R. Huben
Lee Sharkey
MILM
33
344
0
15 Sep 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
Exploring Faithful Rationale for Multi-hop Fact Verification via
  Salience-Aware Graph Learning
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning
Jiasheng Si
Yingjie Zhu
Deyu Zhou
37
12
0
02 Dec 2022
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency
  Methods
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods
Josip Jukić
Martin Tutek
Jan Snajder
FAtt
24
0
0
15 Nov 2022
Exploring Self-Attention for Crop-type Classification Explainability
Exploring Self-Attention for Crop-type Classification Explainability
Ivica Obadic
R. Roscher
Dario Augusto Borges Oliveira
Xiao Xiang Zhu
30
7
0
24 Oct 2022
Evaluating the Faithfulness of Importance Measures in NLP by Recursively
  Masking Allegedly Important Tokens and Retraining
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining
Andreas Madsen
Nicholas Meade
Vaibhav Adlakha
Siva Reddy
111
35
0
15 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
Making Attention Mechanisms More Robust and Interpretable with Virtual
  Adversarial Training
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
Shunsuke Kitada
Hitoshi Iyatomi
AAML
28
8
0
18 Apr 2021
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
287
622
0
04 Dec 2018
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
257
3,690
0
28 Feb 2017
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