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In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot solutions to myriad classic NLP problems.
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 · 1901
Earlier work this paper cites.
Muc-6 named entity task definition (version 2.1)
Nancy Chinchor. 1995 · 1995
Earlier work this paper cites.
Exploiting diverse knowledge sources via maximum entropy in named entity recognition
Andrew Borthwick, John Sterling, Eugene Agichtein, and Ralph Grishman. 1998 · 1998
Earlier work this paper cites.
A maximum entropy approach to named entity recognition
Andrew Eliot Borthwick. 1999 · 1999
Earlier work this paper cites.
Named entity recognition without gazetteers
Andrei Mikheev, Marc Moens, and Claire Grover. 1999 · 1999
Earlier work this paper cites.
Rule-based named entity recognition for greek financial texts
Dimitra Farmakiotou, Vangelis Karkaletsis, John Koutsias, George Sigletos, Constantine D Spyropoulos, and Panagiotis Stamatopoulos. 2000 · 2000
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
Earlier work this paper cites.
Tuning support vector machines for biomedical named entity recognition
Takaki Makino, Yoshihiro Ohta, Jun’ichi Tsujii, et al. 2002 · 2002
Earlier work this paper cites.
Named entity recognition using an hmm-based chunk tagger
GuoDong Zhou and Jian Su. 2002 · 2002
Earlier work this paper cites.
Genia corpus—a semantically annotated corpus for bio-textmining
J-D Kim, Tomoko Ohta, Yuka Tateisi, and Jun’ichi Tsujii. 2003 · 2003
Earlier work this paper cites.
Named entity recognition using hundreds of thousands of features
James Mayfield, Paul McNamee, and Christine Piatko. 2003 · 2003
Earlier work this paper cites.
Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder. 2003 · 2003
Earlier work this paper cites.
Named entity recognition as dependency parsing
Juntao Yu, Bernd Bohnet, and Massimo Poesio. 2020 · 2005
Earlier work this paper cites.
Automated concatenation of embeddings for structured prediction
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, and Kewei Tu. 2020 · 2010
Earlier work this paper cites.
A survey on the application of recurrent neural networks to statistical language modeling
Wim De Mulder, Steven Bethard, and Marie-Francine Moens. 2015 · 2015
Earlier work this paper cites.
Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Cited alongside, same era.
Bidirectional lstm for named entity recognition in twitter messages
Nut Limsopatham and Nigel Henry Collier. 2016 · 2016
Cited alongside, same era.
A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations
Tome Eftimov, Barbara Koroušić Seljak, and Peter Korošec. 2017 · 2017
Cited alongside, same era.
Emerging trends: A gentle introduction to fine-tuning
Kenneth Ward Church, Zeyu Chen, and Yanjun Ma. 2021 · 2021
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.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
Later among the works it cites.
Container: Few-shot named entity recognition via contrastive learning
Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca J Passonneau, and Rui Zhang. 2022 · 2022
Later among the works it cites.
A label-aware autoregressive framework for cross-domain ner
Jinpeng Hu, He Zhao, Dan Guo, Xiang Wan, and Tsung-Hui Chang. 2022a · 2022
Later among the works it cites.
Unified named entity recognition as word-word relation classification
Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, and Fei Li. 2022 · 2022
Later among the works it cites.
Decomposed meta-learning for few-shot named entity recognition
Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, and Chin-Yew Lin. 2022 · 2022
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Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Hai-Tao Zheng, and Zhiyuan Liu. 2021 · 2021
Cited alongside, same era.
Good examples make a faster learner: Simple demonstration-based learning for low-resource ner
Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, and Xiang Ren. 2021 · 2021
Cited alongside, same era.
Crossner: Evaluating cross-domain named entity recognition
Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, and Pascale Fung. 2021 · 2021
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Template-free prompt tuning for few-shot ner
Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Qi Zhang, and Xuanjing Huang. 2021 · 2021
Cited alongside, same era.
Named entity recognition in natural language processing: A systematic review
Abhishek Sharma, Sudeshna Chakraborty, and Shivam Kumar. 2022 · 2021
Cited alongside, same era.
Locate and label: A two-stage identifier for nested named entity recognition
Yongliang Shen, Xinyin Ma, Zeqi Tan, Shuai Zhang, Wen Wang, and Weiming Lu. 2021 · 2021
Cited alongside, same era.
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. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Few-shot nested named entity recognition
Hong Ming, Jiaoyun Yang, Lili Jiang, Yan Pan, and Ning An. 2022 · 2022
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Improving biomedical named entity recognition with a unified multi-task mrc framework
Yiqi Tong, Fuzhen Zhuang, Deqing Wang, Haochao Ying, and Binling Wang. 2022 · 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 · 2022
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From clozing to comprehending: Retrofitting pre-trained language model to pre-trained machine reader
Weiwen Xu, Xin Li, Wenxuan Zhang, Meng Zhou, Lidong Bing, Wai Lam, and Luo Si. 2022 · 2022
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Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, and Yue Zhang. 2022 · 2022
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One model for all domains: Collaborative domain-prefix tuning for cross-domain ner
Xiang Chen, Lei Li, Qiaoshuo Fei, Ningyu Zhang, Chuanqi Tan, Yong Jiang, Fei Huang, and Huajun Chen. 2023 · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. 2023 · 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 · 2023
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