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The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative.
Language models are few-shot learners
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Roberta: A robustly optimized bert pretraining approach
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Attention is not not explanation
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Eraser: A benchmark to evaluate rationalized nlp models
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Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2004
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Towards transparent and explainable attention models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran. 2020 · 2004
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Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace. 2020 · 2005
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An information bottleneck approach for controlling conciseness in rationale extraction
Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 2005
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Why do you think that? exploring faithful sentence-level rationales without supervision
Max Glockner, Ivan Habernal, and Iryna Gurevych. 2020 · 2010
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Fid-ex: Improving sequence-to-sequence models for extractive rationale generation
Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal, Wen-tau Yih, Yashar Mehdad, and Srinivasan Iyer. 2020 · 2012
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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Quaser: Question answering with scalable extractive rationalization
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Roscoe: A suite of metrics for scoring step-by-step reasoning
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Summac: Re-visiting nli-based models for inconsistency detection in summarization
Philippe Laban, Tobias Schnabel, Paul N Bennett, and Marti A Hearst. 2022 · 2022
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Toward practical usage of the attention mechanism as a tool for interpretability
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Chain-of-thought prompting elicits reasoning in large language models
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Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
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Deriving machine attention from human rationales
Yujia Bao, Shiyu Chang, Mo Yu, and Regina Barzilay. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Why attentions may not be interpretable?
Bing Bai, Jian Liang, Guanhua Zhang, Hao Li, Kun Bai, and Fei Wang. 2021 · 2021
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Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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Diagnostics-guided explanation generation
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2022 · 2022
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Can rationalization improve robustness?
Howard Chen, Jacqueline He, Karthik Narasimhan, and Danqi Chen. 2022 · 2022
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A survey on XAI and natural language explanations
Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, and Navid Nobani. 2023 · 2023
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Zara: Improving few-shot self-rationalization for small language models
Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, Cheng-Kuang Wu, and Hsin-Hsi Chen. 2023 · 2023
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Rationalization for explainable nlp: A survey
Sai Gurrapu, Ajay Kulkarni, Lifu Huang, Ismini Lourentzou, Laura Freeman, and Feras A Batarseh. 2023 · 2023
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Summary of chatgpt/gpt-4 research and perspective towards the future of large language models
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al. 2023 · 2023
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Factually consistent summarization via reinforcement learning with textual entailment feedback
Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, et al. 2023 · 2023
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A comprehensive review on financial explainable ai
Wei Jie Yeo, Wihan van der Heever, Rui Mao, Erik Cambria, Ranjan Satapathy, and Gianmarco Mengaldo. 2023 · 2023
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