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Pretrained language models (PLMs) have made significant strides in various natural language processing tasks.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Explaining classifiers with causal concept effect (cace)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim. 2019 · 1907
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Interpretability beyond classification output: Semantic bottleneck networks
Max Losch, Mario Fritz, and Bernt Schiele. 2019 · 1907
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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Local interpretable model-agnostic explanations for music content analysis
Saumitra Mishra, Bob L Sturm, and Simon Dixon. 2017 · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Yedda: A lightweight collaborative text span annotation tool
Jie Yang, Yue Zhang, Linwei Li, and Xingxuan Li. 2017 · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017 · 2017
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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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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al. 2018 · 2018
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Who feels what and why? annotation of a literature corpus with semantic roles of emotions
Evgeny Kim and Roman Klinger. 2018 · 2018
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Analysis methods in neural language processing: A survey
Yonatan Belinkov and James Glass. 2019 · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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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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Attention in natural language processing
Andrea Galassi, Marco Lippi, and Paolo Torroni. 2020 · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020 · 2020
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Compositional explanations of neurons
Jesse Mu and Jacob Andreas. 2020 · 2020
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Machine learning of concepts hard even for humans: The case of online depression forums
Renáta Németh, Domonkos Sik, and Fanni Máté. 2020 · 2020
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Black-box prompt learning for pre-trained language models
Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, and Tong Zhang. 2022 · 2022
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Identifiability of label noise transition matrix
Yang Liu, Hao Cheng, and Kun Zhang. 2022 · 2022
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Post-hoc interpretability for neural nlp: A survey
Andreas Madsen, Siva Reddy, and Sarath Chandar. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Bloom: A 176b-parameter open-access multilingual language model
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. 2020 · 2020
Cited alongside, same era.
Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. 2020 · 2020
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Incorporating bert into neural machine translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tieyan Liu. 2020 · 2020
Cited alongside, same era.
Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions
Hongjie Cai, Rui Xia, and Jianfei Yu. 2021 · 2021
Cited alongside, same era.
Socially responsible ai algorithms: Issues, purposes, and challenges
Lu Cheng, Kush R Varshney, and Huan Liu. 2021 · 2021
Cited alongside, same era.
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
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Mitigating spurious correlation in natural language understanding with counterfactual inference
Can Udomcharoenchaikit, Wuttikorn Ponwitayarat, Patomporn Payoungkhamdee, Kanruethai Masuk, Weerayut Buaphet, Ekapol Chuangsuwanich, and Sarana Nutanong. 2022 · 2022
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Causal proxy models for concept-based model explanations
Zhengxuan Wu, Karel D’Oosterlinck, Atticus Geiger, Amir Zur, and Christopher Potts. 2022 · 2022
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022 · 2022
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Interpreting language models with contrastive explanations
Kayo Yin and Graham Neubig. 2022 · 2022
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Concept embedding models
Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Frederic Precioso, Stefano Melacci, Adrian Weller, Pietro Lio, et al. 2022 · 2022
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A survey on aspect-based sentiment analysis: tasks, methods, and challenges
Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, and Wai Lam. 2022 · 2022
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Language models can explain neurons in language models
Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders. 2023 · 2023
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Chatgpt outperforms crowd-workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
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Ai transparency in the age of llms: A human-centered research roadmap
Q Vera Liao and Jennifer Wortman Vaughan. 2023 · 2023
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Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng. 2023 · 2023
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OpenAI. 2023 · 2023
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Mind meets machine: Unravelling gpt-4’s cognitive psychology
Manmeet Singh, Vaisakh SB, Neetiraj Malviya, et al. 2023 · 2023
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