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Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expensive.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts
Franck Dernoncourt and Ji Young Lee · 2017
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Overview of the biocreative vi chemical-protein interaction track
Martin Krallinger, Obdulia Rabal, Saber Ahmad Akhondi, Martín Pérez Pérez, Jesus Santamaría, Gael Pérez Rodríguez, Georgios Tsatsaronis, Ander Intxaurrondo, J. A. Lopez, Umesh K. Nandal, Erin M. van Buel, Ambika Chandrasekhar, Marleen Rodenburg, Astrid Lægreid, Marius A. Doornenbal, Julen Oyarzábal, Anália Lourenço, and Alfonso Valencia · 2017
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Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 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
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Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun · 2018
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
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Probing biomedical embeddings from language models
Qiao Jin, Bhuwan Dhingra, William W Cohen, and Xinghua Lu · 2019
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Situating sentence embedders with nearest neighbor overlap
Lucy H Lin and Noah A Smith · 2019
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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
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Revisiting fine-tuning for few-shot learning
Akihiro Nakamura and Tatsuya Harada · 2019
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Training complex models with multi-task weak supervision
A. J. Ratner, B. Hancock, J. Dunnmon, F. Sala, S. Pandey, and C. Ré · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Learning dependency structures for weak supervision models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, and Christopher Re · 2019
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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
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Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Re · 2020
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Domain-specific language model pretraining for biomedical natural language processing, 2020
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2020
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Don’t stop pretraining: adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2020
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Huggingface 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, et al · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2021
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Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer · 2021
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CUAD: an expert-annotated NLP dataset for legal contract review
Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning, 2021
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation, 2021
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Jurassic-1: Technical details and evaluation
Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham · 2021
Cited alongside, same era.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex J Andonian, and Yonatan Belinkov · 2022
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Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn · 2022
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Manifest
Laurel Orr · 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, et al · 2022
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Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi · 2021
Cited alongside, same era.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
Cited alongside, same era.
Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models, 2021
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
Cited alongside, same era.
When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings
Lucia Zheng, Neel Guha, Brandon R Anderson, Peter Henderson, and Daniel E Ho · 2021
Cited alongside, same era.
Large language models are few-shot clinical information extractors, 2022
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag · 2022
Cited alongside, same era.
Ask me anything: A simple strategy for prompting language models, 2022
Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré · 2022
Cited alongside, same era.
Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, and Pradeep Ravikumar · 2022
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Bloom: A 176b-parameter open-access multilingual language model
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
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Universalizing weak supervision
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Carl Roberts, and Frederic Sala · 2022
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Selective annotation makes language models better few-shot learners, 2022
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu · 2022
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Lifting weak supervision to structured prediction
Harit Vishwakarma and Frederic Sala · 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
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Learning hyper label model for programmatic weak supervision
Renzhi Wu, Shen-En Chen, Jieyu Zhang, and Xu Chu · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Meerkat and the path to foundation models as a reliable software abstraction
Karan Goel, Sabri Eyuboglu, Arjun Desai, James Zou, and Chris Ré · 2023
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Employers should consider these risks when employees use chatgpt
Karla Grossenbacher · 2023
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Annollm: Making large language models to be better crowdsourced annotators, 2023
Xingwei He, Zhenghao Lin, Yeyun Gong, A-Long Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, and Weizhu Chen · 2023
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Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes, 2023
Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister · 2023
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Taja Kuzman, Igor Mozetic, and Nikola Ljubešic · 2023
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Legal issues taxonomy, 2023
Stanford Legal Design Lab · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Alpaca: A strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Releasing v1 of gpt-jt powered by open-source ai
Together · 2023
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