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Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors.
Language models are few-shot learners
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Stuart Russell. 2015 · 2015
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Towards a rigorous science of interpretable machine learning
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan. 2020 · 2020
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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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Compression, transduction, and creation: A unified framework for evaluating natural language generation
Mingkai Deng, Bowen Tan, Zhengzhong Liu, Eric Xing, and Zhiting Hu. 2021 · 2021
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Counterfactual evaluation for explainable AI
Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, and Yongfeng Zhang. 2021 · 2021
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e-vil: A dataset and benchmark for natural language explanations in vision-language tasks
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton. 2018 · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Make up your mind! adversarial generation of inconsistent natural language explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. 2020 · 2020
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A survey of the state of explainable AI for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. 2020 · 2020
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ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020 · 2020
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Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?
Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal. 2020 · 2020
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Proofver: Natural logic theorem proving for fact verification
Amrith Krishna, Sebastian Riedel, and Andreas Vlachos. 2021 · 2021
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Toward code generation: A survey and lessons from semantic parsing
Celine Lee, Justin Gottschlich, and Dan Roth. 2021 · 2021
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Free Logic
John Nolt. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
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Explaining NLP models via minimal contrastive editing (MiCE)
Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021
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Do natural language explanations represent valid logical arguments? verifying entailment in explainable NLI gold standards
Marco Valentino, Ian Pratt-Hartmann, and André Freitas. 2021 · 2021
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Teach me to explain: A review of datasets for explainable nlp
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A. Smith. 2021 · 2021
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