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Transformer language models are state of the art in a multitude of NLP tasks.
Applications of Computers and Information Technology - 18
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Thumbs up? Sentiment Classification Using Machine Learning Techniques
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”Why Should I Trust You?”: Explaining the Predictions of Any Classifier
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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
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Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Language Models are Unsupervised Multitask Learners
Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I · 2019
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Explanatory interactive machine learning
Teso S, Kersting K · 2019
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Analysis Methods in Neural Language Processing: A Survey
Belinkov Y, Glass J · 2019
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The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Voita E, Sennrich R, Titov I · 2019
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Attention is not Explanation
Jain S, Wallace BC · 2019
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Attention is not not Explanation
Wiegreffe S, Pinter Y · 2019
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Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned
Voita E, Talbot D, Moiseev F, Sennrich R, Titov I · 2019
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This Looks Like That: Deep Learning for Interpretable Image Recognition
Chen C, Li O, Tao D, Barnett A, Rudin C, Su JK · 2019
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Interpretable Image Recognition with Hierarchical Prototypes
Hase P, Chen C, Li O, Rudin C · 2019
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Han X, Wallace BC, Tsvetkov Y · 2020
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The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
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ERASER: A Benchmark to Evaluate Rationalized NLP Models
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh V, Debut L, Chaumond J, Wolf T · 2020
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Evaluating Saliency Methods for Neural Language Models
Ding S, Koehn P · 2021
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An Empirical Comparison of Instance Attribution Methods for NLP
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Interpretable and Steerable Sequence Learning via Prototypes
Ming Y, Xu P, Qu H, Ren L · 2019
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Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded
Selvaraju RR, Lee S, Shen Y, Jin H, Ghosh S, Heck LP, et al · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers N, Gurevych I · 2019
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How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings
Ethayarajh K · 2019
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Schramowski P, Stammer W, Teso S, Brugger A, Shao X, Luigs HG, et al · 2020
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Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches
Sokol K, Flach P · 2020
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Pezeshkpour P, Jain S, Wallace BC, Singh S · 2021
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Bender EM, Gebru T, McMillan-Major A, Shmitchell S · 2021
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What’s in your Head? Emergent Behaviour in Multi-Task Transformer Models
Geva M, Katz U, Ben-Arie A, Berant J · 2021
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Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations
Stammer W, Schramowski P, Kersting K · 2021
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Aligning Faithful Interpretations with their Social Attribution
Jacovi A, Goldberg Y · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, et al · 2021
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Cooperative AI: machines must learn to find common ground
Dafoe A, Bachrach Y, Hadfield G, Horvitz E, Larson K, Graepel T · 2021
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A Typology to Explore and Guide Explanatory Interactive Machine Learning
Friedrich F, Stammer W, Schramowski P, Kersting K · 2022
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