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Despite the impressive capabilities of large language models (LLMs), their performance on information extraction tasks is still not entirely satisfactory.
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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A new readability yardstick
Rudolph Flesch. 1948 · 1948
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin-Yew Lin and Eduard H. Hovy. 2003 · 2003
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Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons
Andrew McCallum and Wei Li. 2003 · 2003
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NLTK: the natural language toolkit
Steven Bird. 2006 · 2006
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Few-shot named entity recognition: A comprehensive study
Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, and Jiawei Han. 2020 · 2012
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Query understanding enhanced by hierarchical parsing structures
Jingjing Liu, Panupong Pasupat, Yining Wang, Scott Cyphers, and James R. Glass. 2013 · 2013
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Ontonotes release 5.0 ldc2013t19
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013 · 2013
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R. Sabuncu. 2018 · 2018
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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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Few-shot classification in named entity recognition task
Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019 · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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EDA: easy data augmentation techniques for boosting performance on text classification tasks
Jason W. Wei and Kai Zou. 2019 · 2019
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Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
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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 · 2020
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Template-free prompt tuning for few-shot NER
Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, and Xuanjing Huang. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey 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 F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Data augmentation for intent classification with off-the-shelf large language models
Gaurav Sahu, Pau Rodríguez, Issam H. Laradji, Parmida Atighehchian, David Vázquez, and Dzmitry Bahdanau. 2022 · 2022
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BLOOM: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilic, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M. Rush, Stella Biderman, Albert Webson, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît Sagot, Niklas Muennighoff, Albert Villanova del Moral, Olatunji Ruwase, Rachel Bawden, Stas Bekman, Angelina McMillan-Major, Iz Beltagy, Huu Nguyen, Lucile Saulnier, Samson Tan, Pedro Ortiz Suarez, Victor Sanh, Hugo Laurençon, Yacine Jernite, Julien Launay, Margaret Mitchell, Colin Raffel, Aaron Gokaslan, Adi Simhi, Aitor Soroa, Alham Fikri Aji, Amit Alfassy, Anna Rogers, Ariel Kreisberg Nitzav, Canwen Xu, Chenghao Mou, Chris Emezue, Christopher Klamm, Colin Leong, Daniel van Strien, David Ifeoluwa Adelani, and et al. 2022 · 2022
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An analysis of simple data augmentation for named entity recognition
Xiang Dai and Heike Adel. 2020 · 2020
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DAGA: data augmentation with a generation approach forlow-resource tagging tasks
Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq R. Joty, Luo Si, and Chunyan Miao. 2020 · 2020
Cited alongside, same era.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020 · 2020
Cited alongside, same era.
Simple and effective few-shot named entity recognition with structured nearest neighbor learning
Yi Yang and Arzoo Katiyar. 2020 · 2020
Cited alongside, same era.
Template-based named entity recognition using BART
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021 · 2021
Cited alongside, same era.
Few-nerd: A few-shot named entity recognition dataset
Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, and Zhiyuan Liu. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
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The study of language
George Yule. 2022 · 2022
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MELM: data augmentation with masked entity language modeling for low-resource NER
Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, and Chunyan Miao. 2022 · 2022
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How robust is GPT-3.5 to predecessors? A comprehensive study on language understanding tasks
Xuanting Chen, Junjie Ye, Can Zu, Nuo Xu, Rui Zheng, Minlong Peng, Jie Zhou, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023 · 2023
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Auggpt: Leveraging chatgpt for text data augmentation
Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Lichao Sun, Quanzheng Li, Dinggang Shen, Tianming Liu, and Xiang Li. 2023 · 2023
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Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
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Instructuie: Multi-task instruction tuning for unified information extraction
Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, Jihua Kang, Jingsheng Yang, Siyuan Li, and Chunsai Du. 2023 · 2023
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A comprehensive capability analysis of GPT-3 and GPT-3.5 series models
Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang Shen, Jie Zhou, Siming Chen, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023 · 2023
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