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The weak-to-strong generalization phenomenon is the driver for important machine learning applications including highly data-efficient learning and, most recently, performing superalignment.
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth · 2018
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Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2018
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“going on a vacation” takes longer than “going for a walk”: A study of temporal commonsense understanding
Qiang Ning Ben Zhou, Daniel Khashabi and Dan Roth · 2019
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
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Mohammad Taher Pilehvar and Jose Camacho-Collados · 2019
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Estimating example difficulty using variance of gradients
Chirag Agarwal, Daniel D’souza, and Sara Hooker · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
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Universalizing weak supervision
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Carl Roberts, and Frederic Sala · 2022
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2022
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An empirical study of gpt-3 for few-shot knowledge-based vqa
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Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi · 2019
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Dream: A challenge data set and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie · 2019
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"quartz: An open-domain dataset of qualitative relationship questions"
Oyvind Tafjord, Matt Gardner, Kevin Lin, and Peter Clark · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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PAWS: Paraphrase Adversaries from Word Scrambling
Yuan Zhang, Jason Baldridge, and Luheng He · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
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Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg · 2022
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Self-training: A survey, 2023
Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto, Lies Hadjadj, Emilie Devijver, and Yury Maximov · 2023
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Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al · 2023
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Compression, generalization and learning
Marco C Campi and Simone Garatti · 2023
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Data selection for language models via importance resampling
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy S Liang · 2023
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Perplexed by perplexity: Perplexity-based data pruning with small reference models
Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L Leavitt, and Mansheej Paul · 2024
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Quantifying the gain in weak-to-strong generalization
Moses Charikar, Chirag Pabbaraju, and Kirankumar Shiragur · 2024
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Weak to strong generalization: Some theoretical perspectives
EleutherAI · 2024
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Theoretical analysis of weak-to-strong generalization, 2024
Hunter Lang, David Sontag, and Aravindan Vijayaraghavan · 2024
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Datacomp-lm: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, et al · 2024
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The pick-to-learn algorithm: empowering compression for tight generalization bounds and improved post-training performance
Dario Paccagnan, Marco C. Campi, and Simone Garatti · 2024
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Nabeel Seedat, Fergus Imrie, and Mihaela van der Schaar · 2024
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A statistical framework for weak-to-strong generalization
Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee, Ya’acov Ritov, Mikhail Yurochkin, and Yuekai Sun · 2024
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Easy-to-hard generalization: Scalable alignment beyond human supervision
Zhiqing Sun, Longhui Yu, Yikang Shen, Weiyang Liu, Yiming Yang, Sean Welleck, and Chuang Gan · 2024
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Qurating: Selecting high-quality data for training language models
Alexander Wettig, Aatmik Gupta, Saumya Malik, and Danqi Chen · 2024
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Less: Selecting influential data for targeted instruction tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen · 2024
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