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Humans work together to solve common problems by having discussions, explaining, and agreeing or disagreeing with each other.
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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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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Sense embeddings are also biased – evaluating social biases in static and contextualised sense embeddings
Yi Zhou, Masahiro Kaneko, and Danushka Bollegala. 2022 · 1935
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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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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. 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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Revolt: Collaborative crowdsourcing for labeling machine learning datasets
Joseph Chee Chang, Saleema Amershi, and Ece Kamar. 2017 · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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MultiWOZ - a Large-Scale Multi-Domain Wizard-of-Oz dataset for Task-Oriented dialogue modelling
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gašić. 2018 · 2018
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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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WorldTree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton Morrison. 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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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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Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. 2018 · 2018
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Hello, it’s GPT-2 - how can I help you? towards the use of pretrained language models for Task-Oriented dialogue systems
Paweł Budzianowski and Ivan Vulić. 2019 · 2019
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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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Gender-preserving debiasing for pre-trained word embeddings
Masahiro Kaneko and Danushka Bollegala. 2019 · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
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End-to-End neural pipeline for Goal-Oriented dialogue systems using GPT-2
Donghoon Ham, Jeong-Gwan Lee, Youngsoo Jang, and Kee-Eung Kim. 2020 · 2020
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NILE : Natural language inference with faithful natural language explanations
Sawan Kumar and Partha Talukdar. 2020 · 2020
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WT5?! training Text-to-Text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020 · 2020
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Fixing model bugs with natural language patches
Shikhar Murty, Christopher D Manning, Scott Lundberg, and Marco Tulio Ribeiro. 2022 · 2022
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Robust speech recognition via large-scale weak supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2022 · 2022
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Correcting robot plans with natural language feedback
Pratyusha Sharma, Balakumar Sundaralingam, Valts Blukis, Chris Paxton, Tucker Hermans, Antonio Torralba, Jacob Andreas, and Dieter Fox. 2022 · 2022
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Talktomodel: Explaining machine learning models with interactive natural language conversations
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. 2022 · 2022
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Do prompt-based models really understand the meaning of their prompts?
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Gender and representation bias in GPT-3 generated stories
Li Lucy and David Bamman. 2021 · 2021
Cited alongside, same era.
Towards zero-label language learning
Zirui Wang, Adams Wei Yu, Orhan Firat, and Yuan Cao. 2021 · 2021
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Improving multimodal interactive agents with reinforcement learning from human feedback
Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Jirka Lhotka, Timothy Lillicrap, Alistair Muldal, et al. 2022 · 2022
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Measuring progress on scalable oversight for large language models
Samuel R Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamile Lukosuite, Amanda Askell, Andy Jones, Anna Chen, et al. 2022 · 2022
Cited alongside, same era.
Gender bias in word embeddings: a comprehensive analysis of frequency, syntax, and semantics
Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth, Robert Wolfe, and Mahzarin R Banaji. 2022 · 2022
Cited alongside, same era.
Training language models with language feedback
Jon Ander Campos and Jun Shern. 2022 · 2022
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Albert Webson and Ellie Pavlick. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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Reframing human-AI collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. 2022 · 2022
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
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Panatchakorn Anantaprayoon, Masahiro Kaneko, and Naoaki Okazaki. 2023 · 2023
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The gaps between pre-train and downstream settings in bias evaluation and debiasing
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