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Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance.
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, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey 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 · 1901
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019 · 1906
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ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts
Yuta Koreeda and Christopher Manning. 2021 · 1919
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Does active learning work? a review of the research
Michael Prince. 2004 · 2004
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A survey of active learning for text classification using deep neural networks
Christopher Schröder and Andreas Niekler. 2020 · 2008
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A literature survey of active machine learning in the context of natural language processing
Fredrik Olsson. 2009 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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A survey on instance selection for active learning
Yifan Fu, Xingquan Zhu, and Bin Li. 2013 · 2013
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Active Learning with Rationales for Text Classification
Manali Sharma, Di Zhuang, and Mustafa Bilgic. 2015 · 2015
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Deep Active Learning for Named Entity Recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar. 2017 · 2017
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Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener and Silvio Savarese. 2018 · 2018
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Low-resource Deep Entity Resolution with Transfer and Active Learning
Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, and Lucian Popa. 2019 · 2019
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CLAUDETTE: An Automated Detector of Potentially Unfair Clauses in Online Terms of Service
Marco Lippi, Przemyslaw Palka, Giuseppe Contissa, Francesca Lagioia, Hans-Wolfgang Micklitz, Giovanni Sartor, and Paolo Torroni. 2019 · 2019
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Explanatory interactive machine learning
Stefano Teso and Kristian Kersting. 2019 · 2019
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BioMRC: A Dataset for Biomedical Machine Reading Comprehension
Dimitris Pappas, Petros Stavropoulos, Ion Androutsopoulos, and Ryan McDonald. 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, and Peter J Liu. 2020 · 2020
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A survey of human-in-the-loop for machine learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. 2022 · 2022
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Nora Bradford, Branda Sun, Tran Bao Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, and Mark Warschauer. 2022 · 2022
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ALLSH: Active learning guided by local sensitivity and hardness
Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022a · 2022
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Can Large Language Models Be an Alternative to Human Evaluations?
Cheng-Han Chiang and Hung-yi Lee. 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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MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020 · 2020
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A survey of deep active learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B Gupta, Xiaojiang Chen, and Xin Wang. 2021 · 2021
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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022 · 2022
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Finetuned Language Models Are Zero-Shot Learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022 · 2022
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023a
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G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
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ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning
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Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
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