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Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as patents, papers, and theses, without having domain-specific knowledge.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020a · 1901
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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. 2020b · 1901
Earlier work this paper cites.
Chemdataextractor: a toolkit for automated extraction of chemical information from the scientific literature
Matthew C Swain and Jacqueline M Cole. 2016 · 1904
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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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 · 2017
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Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019 · 2019
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Applications for deep learning in ecology
Sylvain Christin, Étienne Hervet, and Nicolas Lecomte. 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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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. 2019 · 2019
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Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019 · 2019
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Research topics and trends of endangered species using text mining in korea
Min Su Do, Gabin Choi, Ji Woo Hwang, Ji Yeong Lee, Woo Hyun Hur, Young Su Choi, Seong Ji Son, In Kyeong Kwon, Seung Youp Yoo, and Hyo Kee Nam. 2020 · 2020
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2020 · 2020
Structured information extraction from complex scientific text with fine-tuned large language models
Alexander Dunn, John Dagdelen, Nicholas Walker, Sanghoon Lee, Andrew S. Rosen, Gerbrand Ceder, Kristin Persson, and Anubhav Jain. 2022 · 2022
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Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jimenez Gutierrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su. 2022 · 2022
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Co-training improves prompt-based learning for large language models
Hunter Lang, Monica Agrawal, Yoon Kim, and David Sontag. 2022 · 2022
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Language models in the loop: Incorporating prompting into weak supervision
Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach. 2022 · 2022
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Want to reduce labeling cost? gpt-3 can help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021 · 2021
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Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022 · 2022
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OpenAI. 2023 · 2023
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Universalner: Targeted distillation from large language models for open named entity recognition
Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, and Hoifung Poon. 2023 · 2023
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