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Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting.
Fine-grained sentiment analysis with faithful attention
Ruiqi Zhong, Steven Shao, and Kathleen McKeown. 2019 · 1908
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Notes on the n-person game—ii: The value of an n-person game
Lloyd S Shapley. 1951 · 1951
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Detection of influential observation in linear regression
R Dennis Cook. 1977 · 1977
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard H. Hovy, and Dan Jurafsky. 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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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 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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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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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, and Pradeep Ravikumar. 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. 2019 · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou. 2019 · 2019
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Unsupervised domain adaptation of contextualized embeddings for sequence labeling
Xiaochuang Han and Jacob Eisenstein. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2019 · 2019
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Multi-stage influence function
Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, and Cho-Jui Hsieh. 2020 · 2020
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Selection via proxy: Efficient data selection for deep learning
Cody A. Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei A. Zaharia. 2020 · 2020
Cited alongside, same era.
Don’t stop pretraining: Adapt language models to domains and tasks
Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Xiaodong Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021
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Fastif: Scalable influence functions for efficient model interpretation and debugging
Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, and Caiming Xiong. 2021 · 2021
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Influence tuning: Demoting spurious correlations via instance attribution and instance-driven updates
Xiaochuang Han and Yulia Tsvetkov. 2021 · 2021
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A novel sequential coreset method for gradient descent algorithms
Jiawei Huang, Ru Huang, Wenjie Liu, Nikolaos M. Freris, and Huihua Ding. 2021 · 2021
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Revisiting methods for finding influential examples
Karthikeyan K and Anders Søgaard. 2021 · 2021
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2020
Cited alongside, same era.
Influence functions do not seem to predict usefulness in nlp transfer learning
Vid Kocijan and Samuel R. Bowman · 2020
Cited alongside, same era.
Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff A. Bilmes, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Estimating training data influence by tracking gradient descent
Garima Pruthi, Frederick Liu, Mukund Sundararajan, and Satyen Kale. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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R. Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz. 2021 · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaïd Harchaoui. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
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Nlp from scratch without large-scale pretraining: A simple and efficient framework
Xingcheng Yao, Yanan Zheng, Xiaocong Yang, and Zhilin Yang. 2021 · 2021
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When do you need billions of words of pretraining data?
Yian Zhang, Alex Warstadt, Haau-Sing Li, and Samuel R. Bowman. 2021 · 2021
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Tracing knowledge in language models back to the training data
Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu. 2022 · 2022
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Openprompt: An open-source framework for prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, and Maosong Sun. 2022 · 2022
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Combining feature and instance attribution to detect artifacts
Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, and Byron C. Wallace. 2022 · 2022
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