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Few-shot relation extraction involves identifying the type of relationship between two specific entities within a text, using a limited number of annotated samples.
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
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Empirical methods in information extraction
Claire Cardie. 1997 · 1997
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A review of relation extraction
Nguyen Bach and Sameer Badaskar. 2007 · 2007
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Sachin Pawar, Girish K Palshikar, and Pushpak Bhattacharyya. 2017 · 2017
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FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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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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FewRel 2.0: Towards more challenging few-shot relation classification
Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2019b · 2019
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Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts
Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, and Wei Wang. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Learning from Context or Names? An Empirical Study on Neural Relation Extraction
Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2020 · 2020
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Few-shot relation extraction via Bayesian meta-learning on relation graphs
Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, and Jian Tang. 2020 · 2020
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
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Towards realistic few-shot relation extraction
Sam Brody, Sichao Wu, and Adrian Benton. 2021 · 2021
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From learning-to-match to learning-to-discriminate:global prototype learning for few-shot relation classification
Liu Fangchao, Xiao Xinyan, Yan Lingyong, Lin Hongyu, Han Xianpei, Dai Dai, Wu Hua, and Sun Le. 2021 · 2021
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Exploring task difficulty for few-shot relation extraction
Jiale Han, Bo Cheng, and Wei Lu. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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Entity concept-enhanced few-shot relation extraction
Shan Yang, Yongfei Zhang, Guanglin Niu, Qinghua Zhao, and Shiliang Pu. 2021 · 2021
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Knowledge-enhanced domain adaptation in few-shot relation classification
Jiawen Zhang, Jiaqi Zhu, Yi Yang, Wandong Shi, Congcong Zhang, and Hongan Wang. 2021 · 2021
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 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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True few-shot learning with Prompts—A real-world perspective
Timo Schick and Hinrich Schütze. 2022 · 2022
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2022 · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, et al. 2022 · 2022
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
Cited alongside, same era.
Function-words enhanced attention networks for few-shot inverse relation classification
Chunliu Dou, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng, and Kewen Wang. 2022 · 2022
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Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. 2022 · 2022
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Thinking about GPT-3 in-context learning for biomedical IE? think again
Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, You Chen, Lang Li, Huan Sun, and Yu Su. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Graph-based model generation for few-shot relation extraction
Wanli Li and Tieyun Qian. 2022 · 2022
Cited alongside, same era.
What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022a · 2022
Cited alongside, same era.
Later among the works it cites.
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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Self-adaptive in-context learning
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong. 2022 · 2022
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
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Rethinking document-level relation extraction: A reality check
Jing Li, Yequan Wang, Shuai Zhang, and Min Zhang. 2023 · 2023
Closest in time.
Fairness-guided few-shot prompting for large language models
Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, and Bingzhe Wu. 2023 · 2023
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Gpt-re: In-context learning for relation extraction using large language models
Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu, Haiyue Song, Jiwei Li, and Sadao Kurohashi. 2023 · 2023
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