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Task generalization has been a long standing challenge in Natural Language Processing (NLP).
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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DREAM: A challenge data set and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie · 2019
Earlier work this paper cites.
Reasoning over semantic-level graph for fact checking
Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin · 2019
Earlier work this paper cites.
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, et al · 2020
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Discern: Discourse-aware entailment reasoning network for conversational machine reading
Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven CH Hoi, Caiming Xiong, Irwin King, and Michael Lyu · 2020
Earlier work this paper cites.
Unsupervised text generation by learning from search
Jingjing Li, Zichao Li, Lili Mou, Xin Jiang, Michael Lyu, and Irwin King · 2020
Earlier work this paper cites.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
Earlier work this paper cites.
Openprompt: An open-source framework for prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun · 2021
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
Cited alongside, same era.
WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May · 2021
Cited alongside, same era.
Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu · 2021
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
Exploring low-dimensional intrinsic task subspace via prompt tuning
Yujia Qin, Xiaozhi Wang, Yusheng Su, Yankai Lin, Ning Ding, Zhiyuan Liu, Juanzi Li, Lei Hou, Peng Li, Maosong Sun, et al · 2021
Later among the works it cites.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
Later among the works it cites.
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2021
Later among the works it cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze · 2021
Later among the works it cites.
Useradapter: Few-shot user learning in sentiment analysis
Wanjun Zhong, Duyu Tang, Jiahai Wang, Jian Yin, and Nan Duan · 2021
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Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
Cited alongside, same era.
Compare to the knowledge: Graph neural fake news detection with external knowledge
Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, and Ming Zhou · 2021
Cited alongside, same era.
How many data points is a prompt worth?
Teven Le Scao and Alexander M Rush · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig
Cited in the paper.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang
Cited in the paper.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang
Cited in the paper.
Later among the works it cites.
Delta tuning: A comprehensive study of parameter efficient methods for 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 · 2022
Closest in time.
Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, and Houfeng Wang · 2022
Closest in time.
Zeroprompt: Scaling prompt-based pretraining to 1,000 tasks improves zero-shot generalization
Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Yanggang Wang, Haiyu Li, and Zhilin Yang · 2022
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Proqa: Structural prompt-based pre-training for unified question answering, 2022
Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, and Nan Duan · 2022
Closest in time.