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In-Context Learning (ICL) combined with pre-trained large language models has achieved promising results on various NLP tasks.
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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Ellen M Voorhees and Dawn M Tice. 2000 · 2000
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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On the inference calibration of neural machine translation
Shuo Wang, Zhaopeng Tu, Shuming Shi, and Yang Liu. 2020 · 2005
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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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 · 2018
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Towards empathetic open-domain conversation models: A new benchmark and dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2018 · 2018
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Carer: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Contextualized weak supervision for text classification
Dheeraj Mekala and Jingbo Shang. 2020 · 2020
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
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. 2021 · 2021
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
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
Later among the works it cites.
Self-adaptive in-context learning
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong. 2022 · 2022
Later among the works it cites.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023 · 2023
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Survey of hallucination in natural language generation
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
Inpars: Data augmentation for information retrieval using large language models
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
Cited alongside, same era.
LOPS: Learning order inspired pseudo-label selection for weakly supervised text classification
Dheeraj Mekala, Chengyu Dong, and Jingbo Shang. 2022 · 2022
Cited alongside, same era.
Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al. 2022 · 2022
Cited alongside, same era.
Human-like summarization evaluation with chatgpt
Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan. 2023a
Cited in the paper.
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023b
Cited in the paper.
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Finding supporting examples for in-context learning
Xiaonan Li and Xipeng Qiu. 2023 · 2023
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Selfood: Self-supervised out-of-distribution detection via learning to rank
Dheeraj Mekala, Adithya Samavedhi, Chengyu Dong, and Jingbo Shang. 2023 · 2023
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tasksource: A dataset harmonization framework for streamlined nlp multi-task learning and evaluation
Damien Sileo. 2023 · 2023
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