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Incremental Learning (IL) has been a long-standing problem in both vision and Natural Language Processing (NLP) communities.
Continual learning: A comparative study on how to defy forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. 2019 · 1909
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Decomposing logits distillation for incremental named entity recognition
Duzhen Zhang, Yahan Yu, Feilong Chen, and Xiuyi Chen. 2023c · 1923
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen. 1989 · 1989
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Catastrophic forgetting in connectionist networks
Robert M French. 1999 · 1999
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman. 2005 · 2005
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OntoNotes: The 90% solution
Eduard Hovy, Mitchell Marcus, Martha Palmer, Lance Ramshaw, and Ralph Weischedel. 2006 · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)
Shawn N Murphy, Griffin Weber, Michael Mendis, Vivian Gainer, Henry C Chueh, Susanne Churchill, and Isaac Kohane. 2010 · 2010
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. 2017 · 2017
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Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 2017
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr. 2018 · 2018
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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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Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 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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How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
Kawin Ethayarajh. 2019 · 2019
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. 2019 · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019 · 2019
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.
Lamol: Language modeling for lifelong language learning
Fan-Keng Sun, Cheng-Hao Ho, and Hung-Yi Lee. 2019 · 2019
Cited alongside, same era.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu. 2019 · 2019
Cited alongside, same era.
Better fine-tuning by reducing representational collapse
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta. 2020 · 2020
Cited alongside, same era.
Dark experience for general continual learning: a strong, simple baseline
Probing representation forgetting in supervised and unsupervised continual learning
MohammadReza Davari, Nader Asadi, Sudhir Mudur, Rahaf Aljundi, and Eugene Belilovsky. 2022 · 2022
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Peft: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul, and Benjamin Bossan. 2022 · 2022
Later among the works it cites.
Fine-tuned language models are continual learners
Thomas Scialom, Tuhin Chakrabarty, and Smaranda Muresan. 2022 · 2022
Later among the works it cites.
Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. 2022 · 2022
Later among the works it cites.
Prompt conditioned VAE: Enhancing generative replay for lifelong learning in task-oriented dialogue
Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Jian Sun, and Nevin L. Zhang. 2022 · 2022
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Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara. 2020 · 2020
Cited alongside, same era.
Efficient intent detection with dual sentence encoders
Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, and Ivan Vulić. 2020 · 2020
Cited alongside, same era.
Lifelong language knowledge distillation
Yung-Sung Chuang, Shang-Yu Su, and Yun-Nung Chen. 2020 · 2020
Cited alongside, same era.
Continual relation learning via episodic memory activation and reconsolidation
Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Refining sample embeddings with relation prototypes to enhance continual relation extraction
Li Cui, Deqing Yang, Jiaxin Yu, Chengwei Hu, Jiayang Cheng, Jingjie Yi, and Yanghua Xiao. 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.
Distilling causal effect from miscellaneous other-class for continual named entity recognition
Junhao Zheng, Zhanxian Liang, Haibin Chen, and Qianli Ma. 2022 · 2022
Later among the works it cites.
Forward compatible few-shot class-incremental learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan. 2022 · 2022
Later among the works it cites.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al. 2023 · 2023
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Teamwork is not always good: An empirical study of classifier drift in class-incremental information extraction
Minqian Liu and Lifu Huang. 2023 · 2023
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Learning “O” helps for learning more: Handling the unlabeled entity problem for class-incremental NER
Ruotian Ma, Xuanting Chen, Zhang Lin, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, and Yun Wen Chen. 2023 · 2023
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OpenAI. 2023 · 2023
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Semiparametric language models are scalable continual learners
Guangyue Peng, Tao Ge, Si-Qing Chen, Furu Wei, and Houfeng Wang. 2023 · 2023
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Progressive prompts: Continual learning for language models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi. 2023 · 2023
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Class-incremental learning based on label generation
Yijia Shao, Yiduo Guo, Dongyan Zhao, and Bing Liu. 2023 · 2023
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Can bert refrain from forgetting on sequential tasks? a probing study
Mingxu Tao, Yansong Feng, and Dongyan Zhao. 2023 · 2023
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Transformers as support vector machines
Davoud Ataee Tarzanagh, Yingcong Li, Christos Thrampoulidis, and Samet Oymak. 2023 · 2023
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A neural span-based continual named entity recognition model
Yunan Zhang and Qingcai Chen. 2023 · 2023
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Preserving commonsense knowledge from pre-trained language models via causal inference
Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, and Haibin Chen. 2023 · 2023
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Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu. 2023 · 2023
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Incremental sequence labeling: A tale of two shifts
Shengjie Qiu, Junhao Zheng, Zhen Liu, Yicheng Luo, and Qianli Ma. 2024 · 2024
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