Few-shot Learning Literature

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This page categorizes the literature by the Published Venue.

Outline

ACL

Prompt-free and Efficient Few-shot Learning with Language Models ,
by Karimi Mahabadi, Rabeeh and Zettlemoyer, Luke and Henderson, James and Mathias, Lambert and Saeidi, Marzieh and Stoyanov, Veselin and Yazdani, Majid [bib]
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karimi-mahabadi-etal-2022-prompt
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CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning ,
by Das, Sarkar Snigdha Sarathi and Katiyar, Arzoo and Passonneau, Rebecca and Zhang, Rui [bib]
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das-etal-2022-container
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Few-Shot Class-Incremental Learning for Named Entity Recognition ,
by Wang, Rui and Yu, Tong and Zhao, Handong and Kim, Sungchul and Mitra, Subrata and Zhang, Ruiyi and Henao, Ricardo [bib]
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wang-etal-2022-shot
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Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation ,
by Qin, Chengwei and Joty, Shafiq [bib]
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qin-joty-2022-continual
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A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models ,
by Jin, Woojeong and Cheng, Yu and Shen, Yelong and Chen, Weizhu and Ren, Xiang [bib]
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jin-etal-2022-good
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Memorisation versus Generalisation in Pre-trained Language Models ,
by T{\"a}nzer, Michael and Ruder, Sebastian and Rei, Marek [bib]
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tanzer-etal-2022-memorisation
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FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning ,
by Zhou, Jing and Zheng, Yanan and Tang, Jie and Jian, Li and Yang, Zhilin [bib]
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zhou-etal-2022-flipda
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Prototypical Verbalizer for Prompt-based Few-shot Tuning ,
by Cui, Ganqu and Hu, Shengding and Ding, Ning and Huang, Longtao and Liu, Zhiyuan [bib]
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cui-etal-2022-prototypical
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A Rationale-Centric Framework for Human-in-the-loop Machine Learning ,
by Lu, Jinghui and Yang, Linyi and Namee, Brian and Zhang, Yue [bib]
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lu-etal-2022-rationale
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Few-Shot Learning with Siamese Networks and Label Tuning ,
by M{\"u}ller, Thomas and P{\'e}rez-Torr{\'o}, Guillermo and Franco-Salvador, Marc [bib]
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muller-etal-2022-shot
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PPT: Pre-trained Prompt Tuning for Few-shot Learning ,
by Gu, Yuxian and Han, Xu and Liu, Zhiyuan and Huang, Minlie [bib]
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gu-etal-2022-ppt
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Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task ,
by Tabasi, Mohsen and Rezaee, Kiamehr and Pilehvar, Mohammad Taher [bib]
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tabasi-etal-2022-exploiting
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Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection ,
by Shirong Shen and Tongtong Wu and Guilin Qi and Yuan{-}Fang Li and Gholamreza Haffari and Sheng Bi [bib]
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ShenWQLHB21
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ,
by Zhao, Mengjie and Zhu, Yi and Shareghi, Ehsan and Vuli{\'c}, Ivan and Reichart, Roi and Korhonen, Anna and Sch{\"u}tze, Hinrich [bib]
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zhao-etal-2021-closer
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Few-Shot Question Answering by Pretraining Span Selection ,
by Ram, Ori and Kirstain, Yuval and Berant, Jonathan and Globerson, Amir and Levy, Omer [bib]
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ram-etal-2021-shot
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Few-NERD: A Few-shot Named Entity Recognition Dataset ,
by Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Haitao and Liu, Zhiyuan [bib]
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ding-etal-2021-nerd
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Making Pre-trained Language Models Better Few-shot Learners ,
by Gao, Tianyu and Fisch, Adam and Chen, Danqi [bib]
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gao-etal-2021-making
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Distinct Label Representations for Few-Shot Text Classification ,
by Ohashi, Sora and Takayama, Junya and Kajiwara, Tomoyuki and Arase, Yuki [bib]
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ohashi-etal-2021-distinct
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AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation ,
by Xu, Xinnuo and Wang, Guoyin and Kim, Young-Bum and Lee, Sungjin [bib]
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xu-etal-2021-augnlg
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Multi-Label Few-Shot Learning for Aspect Category Detection ,
by Hu, Mengting and Zhao, Shiwan and Guo, Honglei and Xue, Chao and Gao, Hang and Gao, Tiegang and Cheng, Renhong and Su, Zhong [bib]
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hu-etal-2021-multi-label
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Lexicon Learning for Few Shot Sequence Modeling ,
by Akyurek, Ekin and Andreas, Jacob [bib]
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akyurek-andreas-2021-lexicon
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Entity Concept-enhanced Few-shot Relation Extraction ,
by Yang, Shan and Zhang, Yongfei and Niu, Guanglin and Zhao, Qinghua and Pu, Shiliang [bib]
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yang-etal-2021-entity
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Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition ,
by Tong, Meihan and Wang, Shuai and Xu, Bin and Cao, Yixin and Liu, Minghui and Hou, Lei and Li, Juanzi [bib]
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tong-etal-2021-learning
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On Training Instance Selection for Few-Shot Neural Text Generation ,
by Chang, Ernie and Shen, Xiaoyu and Yeh, Hui-Syuan and Demberg, Vera [bib]
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chang-etal-2021-training
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Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations ,
by Coope, Samuel and Farghly, Tyler and Gerz, Daniela and Vuli{\'c}, Ivan and Henderson, Matthew [bib]
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coope-etal-2020-span
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Few-Shot NLG with Pre-Trained Language Model ,
by Chen, Zhiyu and Eavani, Harini and Chen, Wenhu and Liu, Yinyin and Wang, William Yang [bib]
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chen-etal-2020-shot
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Dynamic Memory Induction Networks for Few-Shot Text Classification ,
by Geng, Ruiying and Li, Binhua and Li, Yongbin and Sun, Jian and Zhu, Xiaodan [bib]
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geng-etal-2020-dynamic
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Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network ,
by Hou, Yutai and Che, Wanxiang and Lai, Yongkui and Zhou, Zhihan and Liu, Yijia and Liu, Han and Liu, Ting [bib]
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hou-etal-2020-shot
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Shaping Visual Representations with Language for Few-Shot Classification ,
by Mu, Jesse and Liang, Percy and Goodman, Noah [bib]
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mu-etal-2020-shaping
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Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks ,
by Song, Yiping and Liu, Zequn and Bi, Wei and Yan, Rui and Zhang, Ming [bib]
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song-etal-2020-learning
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Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering ,
by Yan, Ming and Zhang, Hao and Jin, Di and Zhou, Joey Tianyi [bib]
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yan-etal-2020-multi-source
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Discrete Latent Variable Representations for Low-Resource Text Classification ,
by Jin, Shuning and Wiseman, Sam and Stratos, Karl and Livescu, Karen [bib]
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jin-etal-2020-discrete
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Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling ,
by Kruengkrai, Canasai and Nguyen, Thien Hai and Aljunied, Sharifah Mahani and Bing, Lidong [bib]
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kruengkrai-etal-2020-improving
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Soft Gazetteers for Low-Resource Named Entity Recognition ,
by Rijhwani, Shruti and Zhou, Shuyan and Neubig, Graham and Carbonell, Jaime [bib]
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rijhwani-etal-2020-soft
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Matching the Blanks: Distributional Similarity for Relation Learning ,
by Livio Baldini Soares and Nicholas FitzGerald and Jeffrey Ling and Tom Kwiatkowski [bib]
MTB
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SoaresFLK19
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Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification ,
by Zhi{-}Xiu Ye and Zhen{-}Hua Ling [bib]
MLMAN
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YeL19
EMNLP
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MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction ,
by Dong, Manqing and Pan, Chunguang and Luo, Zhipeng [bib]
proposing a label-aware method for low-resource relation extraction
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dong-etal-2021-mapre
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Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning ,
by Zhang, Jianguo and Bui, Trung and Yoon, Seunghyun and Chen, Xiang and Liu, Zhiwei and Xia, Congying and Tran, Quan Hung and Chang, Walter and Yu, Philip [bib]
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zhang-etal-2021-shot
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Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems ,
by Mi, Fei and Zhou, Wanhao and Kong, Lingjing and Cai, Fengyu and Huang, Minlie and Faltings, Boi [bib]
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mi-etal-2021-self
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Nearest Neighbour Few-Shot Learning for Cross-lingual Classification ,
by Bari, M Saiful and Haider, Batool and Mansour, Saab [bib]
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bari-etal-2021-nearest
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TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification ,
by Wang, Chengyu and Wang, Jianing and Qiu, Minghui and Huang, Jun and Gao, Ming [bib]
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wang-etal-2021-transprompt
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Towards Realistic Few-Shot Relation Extraction ,
by Brody, Sam and Wu, Sichao and Benton, Adrian [bib]
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brody-etal-2021-towards
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Exploring Task Difficulty for Few-Shot Relation Extraction ,
by Han, Jiale and Cheng, Bo and Lu, Wei [bib]
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han-etal-2021-exploring
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Learning Prototype Representations Across Few-Shot Tasks for Event Detection ,
by Lai, Viet Dac and Dernoncourt, Franck and Nguyen, Thien Huu [bib]
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lai-etal-2021-learning
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Language Models are Few-Shot Butlers ,
by Micheli, Vincent and Fleuret, Francois [bib]
proposing to use RL and few-shot supervised learning for text generation.
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micheli-fleuret-2021-language
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Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ,
by Chen, Jiawei and Lin, Hongyu and Han, Xianpei and Sun, Le [bib]
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chen-etal-2021-honey
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CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP ,
by Ye, Qinyuan and Lin, Bill Yuchen and Ren, Xiang [bib]
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ye-etal-2021-crossfit
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Constrained Language Models Yield Few-Shot Semantic Parsers ,
by Shin, Richard and Lin, Christopher and Thomson, Sam and Chen, Charles and Roy, Subhro and Platanios, Emmanouil Antonios and Pauls, Adam and Klein, Dan and Eisner, Jason and Van Durme, Benjamin [bib]
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shin-etal-2021-constrained
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Improving and Simplifying Pattern Exploiting Training ,
by Tam, Derek and R. Menon, Rakesh and Bansal, Mohit and Srivastava, Shashank and Raffel, Colin [bib]
proposing ADAPET which promisingly improves the data efficiency of PET. ADAPET does not leverage unlabelled data for training, and introduces label-conditioned loss for the denser supervision.
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tam-etal-2021-improving
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Self-training with Few-shot Rationalization ,
by Bhat, Meghana Moorthy and Sordoni, Alessandro and Mukherjee, Subhabrata [bib]
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bhat-etal-2021-self
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Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction ,
by Sainz, Oscar and Lopez de Lacalle, Oier and Labaka, Gorka and Barrena, Ander and Agirre, Eneko [bib]
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sainz-etal-2021-label
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Continual Few-Shot Learning for Text Classification ,
by Pasunuru, Ramakanth and Stoyanov, Veselin and Bansal, Mohit [bib]
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pasunuru-etal-2021-continual
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Few-Shot Named Entity Recognition: An Empirical Baseline Study ,
by Huang, Jiaxin and Li, Chunyuan and Subudhi, Krishan and Jose, Damien and Balakrishnan, Shobana and Chen, Weizhu and Peng, Baolin and Gao, Jianfeng and Han, Jiawei [bib]
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huang-etal-2021-shot
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STraTA: Self-Training with Task Augmentation for Better Few-shot Learning ,
by Vu, Tu and Luong, Minh-Thang and Le, Quoc and Simon, Grady and Iyyer, Mohit [bib]
pretrained language model-based self-training and data agumentation for few-shot learning.
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vu-etal-2021-strata
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FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models ,
by Chada, Rakesh and Natarajan, Pradeep [bib]
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chada-natarajan-2021-fewshotqa
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Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning ,
by Utama, Prasetya and Moosavi, Nafise Sadat and Sanh, Victor and Gurevych, Iryna [bib]
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utama-etal-2021-avoiding
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Revisiting Self-training for Few-shot Learning of Language Model ,
by Chen, Yiming and Zhang, Yan and Zhang, Chen and Lee, Grandee and Cheng, Ran and Li, Haizhou [bib]
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chen-etal-2021-revisiting
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Open Aspect Target Sentiment Classification with Natural Language Prompts ,
by Seoh, Ronald and Birle, Ian and Tak, Mrinal and Chang, Haw-Shiuan and Pinette, Brian and Hough, Alfred [bib]
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seoh-etal-2021-open
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FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation ,
by Lakhotia, Kushal and Paranjape, Bhargavi and Ghoshal, Asish and Yih, Scott and Mehdad, Yashar and Iyer, Srini [bib]
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lakhotia-etal-2021-fid
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Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks ,
by Guibon, Ga{\"e}l and Labeau, Matthieu and Flamein, H{\'e}l{`e}ne and Lefeuvre, Luce and Clavel, Chlo{\'e} [bib]
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guibon-etal-2021-shot
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts ,
by Shin, Taylor and Razeghi, Yasaman and Logan IV, Robert L. and Wallace, Eric and Singh, Sameer [bib]
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shin-etal-2020-autoprompt
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Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks ,
by Bansal, Trapit and Jha, Rishikesh and Munkhdalai, Tsendsuren and McCallum, Andrew [bib]
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bansal-etal-2020-self
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Adaptive Attentional Network for Few-Shot Knowledge Graph Completion ,
by Sheng, Jiawei and Guo, Shu and Chen, Zhenyu and Yue, Juwei and Wang, Lihong and Liu, Tingwen and Xu, Hongbo [bib]
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sheng-etal-2020-adaptive
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Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs ,
by Lu, Jueqing and Du, Lan and Liu, Ming and Dipnall, Joanna [bib]
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lu-etal-2020-multi
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Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models ,
by Wilcox, Ethan and Qian, Peng and Futrell, Richard and Kohita, Ryosuke and Levy, Roger and Ballesteros, Miguel [bib]
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wilcox-etal-2020-structural
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Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference ,
by Zhang, Jianguo and Hashimoto, Kazuma and Liu, Wenhao and Wu, Chien-Sheng and Wan, Yao and Yu, Philip and Socher, Richard and Xiong, Caiming [bib]
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zhang-etal-2020-discriminative
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Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning ,
by Hua, Yuncheng and Li, Yuan-Fang and Haffari, Gholamreza and Qi, Guilin and Wu, Tongtong [bib]
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hua-etal-2020-shot
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Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning ,
by Yang, Yi and Katiyar, Arzoo [bib]
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yang-katiyar-2020-simple
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An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels ,
by Chalkidis, Ilias and Fergadiotis, Manos and Kotitsas, Sotiris and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion [bib]
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chalkidis-etal-2020-empirical
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Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start ,
by Yin, Wenpeng and Rajani, Nazneen Fatema and Radev, Dragomir and Socher, Richard and Xiong, Caiming [bib]
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yin-etal-2020-universal
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Few-shot Natural Language Generation for Task-Oriented Dialog ,
by Peng, Baolin and Zhu, Chenguang and Li, Chunyuan and Li, Xiujun and Li, Jinchao and Zeng, Michael and Gao, Jianfeng [bib]
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peng-etal-2020-shot
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Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection ,
by Nguyen, Hoang and Zhang, Chenwei and Xia, Congying and Yu, Philip [bib]
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nguyen-etal-2020-dynamic
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Composed Variational Natural Language Generation for Few-shot Intents ,
by Xia, Congying and Xiong, Caiming and Yu, Philip and Socher, Richard [bib]
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xia-etal-2020-composed
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FewRel 2.0: Towards More Challenging Few-Shot Relation Classification ,
by Tianyu Gao and Xu Han and Hao Zhu and Zhiyuan Liu and Peng Li and Maosong Sun and Jie Zhou [bib]
Fewrel 2.0 dataset
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GaoHZLLSZ19
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FewRel: A Large-Scale Supervised Few-shot Relation Classification Dataset with State-of-the-Art Evaluation ,
by Xu Han and Hao Zhu and Pengfei Yu and Ziyun Wang and Yuan Yao and Zhiyuan Liu and Maosong Sun [bib]
FewRel dataset
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HanZYWYLS18
NAACL
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LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification ,
by Hong, S. K. and Jang, Tae Young [bib]
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hong-jang-2022-lea
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On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data ,
by Ahuja, Kabir and Choudhury, Monojit and Dandapat, Sandipan [bib]
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ahuja-etal-2022-economics
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Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization ,
by Zhang, Haode and Liang, Haowen and Zhang, Yuwei and Zhan, Li-Ming and Wu, Xiao-Ming and Lu, Xiaolei and Lam, Albert [bib]
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zhang-etal-2022-fine
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Improving In-Context Few-Shot Learning via Self-Supervised Training ,
by Chen, Mingda and Du, Jingfei and Pasunuru, Ramakanth and Mihaylov, Todor and Iyer, Srini and Stoyanov, Veselin and Kozareva, Zornitsa [bib]
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chen-etal-2022-improving
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An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling ,
by Wang, Peiyi and Xu, Runxin and Liu, Tianyu and Zhou, Qingyu and Cao, Yunbo and Chang, Baobao and Sui, Zhifang [bib]
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wang-etal-2022-enhanced
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MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification ,
by Zhang, Jianhai and Maimaiti, Mieradilijiang and Xing, Gao and Zheng, Yuanhang and Zhang, Ji [bib]
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zhang-etal-2022-mgimn
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Reframing Human-AI Collaboration for Generating Free-Text Explanations ,
by Wiegreffe, Sarah and Hessel, Jack and Swayamdipta, Swabha and Riedl, Mark and Choi, Yejin [bib]
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wiegreffe-etal-2022-reframing
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Few-Shot Document-Level Relation Extraction ,
by Popovic, Nicholas and F{\"a}rber, Michael [bib]
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popovic-farber-2022-shot
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Template-free Prompt Tuning for Few-shot NER ,
by Ma, Ruotian and Zhou, Xin and Gui, Tao and Tan, Yiding and Li, Linyang and Zhang, Qi and Huang, Xuanjing [bib]
```
ma-etal-2022-template
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MetaICL: Learning to Learn In Context ,
by Min, Sewon and Lewis, Mike and Zettlemoyer, Luke and Hajishirzi, Hannaneh [bib]
```
min-etal-2022-metaicl
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Contrastive Learning for Prompt-based Few-shot Language Learners ,
by Jian, Yiren and Gao, Chongyang and Vosoughi, Soroush [bib]
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jian-etal-2022-contrastive
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Embedding Hallucination for Few-shot Language Fine-tuning ,
by Jian, Yiren and Gao, Chongyang and Vosoughi, Soroush [bib]
```
jian-etal-2022-embedding
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Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification ,
by Wang, Han and Xu, Canwen and McAuley, Julian [bib]
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wang-etal-2022-automatic
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DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference ,
by Murty, Shikhar and Hashimoto, Tatsunori B. and Manning, Christopher [bib]
```
murty-etal-2021-dreca
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Learning How to Ask: Querying LMs with Mixtures of Soft Prompts ,
by Qin, Guanghui and Eisner, Jason [bib]
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qin-eisner-2021-learning
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Factual Probing Is [MASK]: Learning vs. Learning to Recall ,
by Zhong, Zexuan and Friedman, Dan and Chen, Danqi [bib]
```
zhong-etal-2021-factual
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It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners ,
by Schick, Timo and Sch{\"u}tze, Hinrich [bib]
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schick-schutze-2021-just
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Few-shot Intent Classification and Slot Filling with Retrieved Examples ,
by Yu, Dian and He, Luheng and Zhang, Yuan and Du, Xinya and Pasupat, Panupong and Li, Qi [bib]
```
yu-etal-2021-shot
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Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System ,
by Xia, Congying and Yin, Wenpeng and Feng, Yihao and Yu, Philip [bib]
```
xia-etal-2021-incremental
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Towards Few-shot Fact-Checking via Perplexity ,
by Lee, Nayeon and Bang, Yejin and Madotto, Andrea and Fung, Pascale [bib]
```
lee-etal-2021-towards
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Knowledge Guided Metric Learning for Few-Shot Text Classification ,
by Sui, Dianbo and Chen, Yubo and Mao, Binjie and Qiu, Delai and Liu, Kang and Zhao, Jun [bib]
```
sui-etal-2021-knowledge
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ConVEx: Data-Efficient and Few-Shot Slot Labeling ,
by Henderson, Matthew and Vuli{\'c}, Ivan [bib]
```
henderson-vulic-2021-convex
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Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning ,
by Wei, Jason and Huang, Chengyu and Vosoughi, Soroush and Cheng, Yu and Xu, Shiqi [bib]
```
wei-etal-2021-shot
COLING
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Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction ,
by Haiyang Yu and Ningyu Zhang and Shumin Deng and Hongbin Ye and Wei Zhang and Huajun Chen [bib]
```
YuZDYZC20
EACL
```
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference ,
by Schick, Timo and Sch{\"u}tze, Hinrich [bib]
```
schick-schutze-2021-exploiting
ICML
```
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation ,
by Giannone, Giorgio and Winther, Ole [bib]
```
pmlr-v162-giannone22a
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Channel Importance Matters in Few-Shot Image Classification ,
by Luo, Xu, Xu, Jing and Xu, Zenglin [bib]
```
pmlr-v162-luo22c
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Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold ,
by Sharma, Sugandha, Chandra, Sarthak and Fiete, Ila [bib]
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pmlr-v162-sharma22b
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Prompting Decision Transformer for Few-Shot Policy Generalization ,
by Xu, Mengdi, Shen, Yikang, Zhang, Shun, Lu, Yuchen, Zhao, Ding, Tenenbaum, Joshua and Gan, Chuang [bib]
```
pmlr-v162-xu22g
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HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning ,
by Zhmoginov, Andrey, Sandler, Mark and Vladymyrov, Maksym [bib]
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pmlr-v162-zhmoginov22a
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Attentional Meta-learners for Few-shot Polythetic Classification ,
by Day, Ben J, Torn{\'e}, Ramon Vi{\~n}as, Simidjievski, Nikola and Li{\'o}, Pietro [bib]
```
pmlr-v162-day22a
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Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification ,
by Lee, Dong Hoon and Chung, Sae-Young [bib]
```
pmlr-v139-lee21d
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Large-Scale Meta-Learning with Continual Trajectory Shifting ,
by Shin, Jaewoong, Lee, Hae Beom, Gong, Boqing and Hwang, Sung Ju [bib]
```
pmlr-v139-shin21a
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Few-shot Language Coordination by Modeling Theory of Mind ,
by Zhu, Hao, Neubig, Graham and Bisk, Yonatan [bib]
```
pmlr-v139-zhu21d
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Calibrate Before Use: Improving Few-shot Performance of Language Models ,
by Zhao, Zihao, Wallace, Eric, Feng, Shi, Klein, Dan and Singh, Sameer [bib]
```
pmlr-v139-zhao21c
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Few-Shot Neural Architecture Search ,
by Zhao, Yiyang, Wang, Linnan, Tian, Yuandong, Fonseca, Rodrigo and Guo, Tian [bib]
```
pmlr-v139-zhao21d
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Learning a Universal Template for Few-shot Dataset Generalization ,
by Triantafillou, Eleni, Larochelle, Hugo, Zemel, Richard and Dumoulin, Vincent [bib]
```
pmlr-v139-triantafillou21a
```
Parameterless Transductive Feature Re-representation for Few-Shot Learning ,
by Cui, Wentao and Guo, Yuhong [bib]
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pmlr-v139-cui21a
```
How Important is the Train-Validation Split in Meta-Learning? ,
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Few-Shot Conformal Prediction with Auxiliary Tasks ,
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A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning ,
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Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks ,
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pmlr-v119-goldblum20a
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Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs ,
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pmlr-v119-qu20a
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Few-shot Domain Adaptation by Causal Mechanism Transfer ,
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LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning ,
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pmlr-v97-li19c
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Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners ,
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Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples ,
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LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING ,
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Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions ,
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Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data ,
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LiSLZZCS19
```
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation ,
by Risto Vuorio and Shao{-}Hua Sun and Hexiang Hu and Joseph J. Lim [bib]
```
VuorioSHL19
```
Self-Supervised Generalisation with Meta Auxiliary Learning ,
by Shikun Liu and Andrew J. Davison and Edward Johns [bib]
```
LiuDJ19
```
Adaptive Gradient-Based Meta-Learning Methods ,
by Mikhail Khodak and Maria{-}Florina Balcan and Ameet S. Talwalkar [bib]
```
KhodakBT19
```
Meta Learning with Relational Information for Short Sequences ,
by Yujia Xie and Haoming Jiang and Feng Liu and Tuo Zhao and Hongyuan Zha [bib]
```
XieJLZZ19
```
TADAM: Task dependent adaptive metric for improved few-shot learning ,
by Boris N. Oreshkin and Pau Rodr{\'{\i}}guez L{\'{o}}pez and Alexandre Lacoste [bib]
```
OreshkinLL18
```
Learning To Learn Around A Common Mean ,
by Giulia Denevi and Carlo Ciliberto and Dimitris Stamos and Massimiliano Pontil [bib]
```
DeneviCSP18
```
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks ,
by Hang Gao and Zheng Shou and Alireza Zareian and Hanwang Zhang and Shih{-}Fu Chang [bib]
```
GaoSZZC18
```
Few-Shot Learning Through an Information Retrieval Lens ,
by Eleni Triantafillou and Richard S. Zemel and Raquel Urtasun [bib]
```
TriantafillouZU17
```
Prototypical Networks for Few-shot Learning ,
by Jake Snell and Kevin Swersky and Richard S. Zemel [bib]
```
SnellSZ17
```
Few-Shot Adversarial Domain Adaptation ,
by Saeid Motiian and Quinn Jones and Seyed Mehdi Iranmanesh and Gianfranco Doretto [bib]
```
MotiianJID17
```
Matching Networks for One Shot Learning ,
by Oriol Vinyals and Charles Blundell and Tim Lillicrap and Koray Kavukcuoglu and Daan Wierstra [bib]
MatchNet
```
VinyalsBLKW16
AAAI
```
FL-MSRE: A Few-Shot Learning based Approach to Multimodal Social Relation Extraction ,
by Hai Wan and Manrong Zhang and Jianfeng Du and Ziling Huang and Yufei Yang and Jeff Z. Pan [bib]
FL-MSRE
```
WanZDHYP21
```
Neural Snowball for Few-Shot Relation Learning ,
by Tianyu Gao and Xu Han and Ruobing Xie and Zhiyuan Liu and Fen Lin and Leyu Lin and Maosong Sun [bib]
Neural Snowball
```
GaoHX0LLS20
```
Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification ,
by Tianyu Gao and Xu Han and Zhiyuan Liu and Maosong Sun [bib]
HATT
```
GaoH0S19
IJCAI
```
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation ,
by Wang, Haoqing and Deng, Zhi-Hong [bib]
```
ijcai2021-149
```
Self-supervised Network Evolution for Few-shot Classification ,
by Tang, Xuwen, Teng, Zhu, Zhang, Baopeng and Fan, Jianping [bib]
```
ijcai2021-419
```
Conditional Self-Supervised Learning for Few-Shot Classification ,
by An, Yuexuan, Xue, Hui, Zhao, Xingyu and Zhang, Lu [bib]
```
ijcai2021-295
```
Few-Shot Partial-Label Learning ,
by Zhao, Yunfeng, Yu, Guoxian, Liu, Lei, Yan, Zhongmin, Cui, Lizhen and Domeniconi, Carlotta [bib]
```
ijcai2021-475
```
Uncertainty-Aware Few-Shot Image Classification ,
by Zhang, Zhizheng, Lan, Cuiling, Zeng, Wenjun, Chen, Zhibo and Chang, Shih-Fu [bib]
```
ijcai2021-471
```
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images ,
by Chen, Wentao, Si, Chenyang, Wang, Wei, Wang, Liang, Wang, Zilei and Tan, Tieniu [bib]
```
ijcai2021-313
SIGIR
```
Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection ,
by Lai, Viet Dac, Nguyen, Minh Van, Nguyen, Thien Huu and Dernoncourt, Franck [bib]
```
3404835.3463054
```
Pseudo Siamese Network for Few-Shot Intent Generation ,
by Xia, Congying, Xiong, Caiming and Yu, Philip [bib]
```
3404835.3462995
KDD
```
Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification ,
by Zhang, Jiawen, Zhu, Jiaqi, Yang, Yi, Shi, Wandong, Zhang, Congcong and Wang, Hongan [bib]
```
3447548.3467438
```
Meta Self-Training for Few-Shot Neural Sequence Labeling ,
by Wang, Yaqing, Mukherjee, Subhabrata, Chu, Haoda, Tu, Yuancheng, Wu, Ming, Gao, Jing and Awadallah, Ahmed Hassan [bib]
```
3447548.3467235
CVPR
```
Prototypical Cross-Domain Self-Supervised Learning for Few-Shot Unsupervised Domain Adaptation ,
by Yue, Xiangyu, Zheng, Zangwei, Zhang, Shanghang, Gao, Yang, Darrell, Trevor, Keutzer, Kurt and Vincentelli, Alberto Sangiovanni [bib]
```
Yue_2021_CVPR
```
Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss ,
by Zhang, Lu, Zhou, Shuigeng, Guan, Jihong and Zhang, Ji [bib]
```
Zhang_2021_CVPR
```
Generalized Few-Shot Object Detection Without Forgetting ,
by Fan, Zhibo, Ma, Yuchen, Li, Zeming and Sun, Jian [bib]
```
Fan_2021_CVPR
```
Hallucination Improves Few-Shot Object Detection ,
by Zhang, Weilin and Wang, Yu-Xiong [bib]
```
Zhang_2021_CVPR
```
Few-Shot Incremental Learning With Continually Evolved Classifiers ,
by Zhang, Chi, Song, Nan, Lin, Guosheng, Zheng, Yun, Pan, Pan and Xu, Yinghui [bib]
```
Zhang_2021_CVPR
```
Rethinking Class Relations: Absolute-Relative Supervised and Unsupervised Few-Shot Learning ,
by Zhang, Hongguang, Koniusz, Piotr, Jian, Songlei, Li, Hongdong and Torr, Philip H. S. [bib]
```
Zhang_2021_CVPR
```
Prototype Completion With Primitive Knowledge for Few-Shot Learning ,
by Zhang, Baoquan, Li, Xutao, Ye, Yunming, Huang, Zhichao and Zhang, Lisai [bib]
```
Zhang_2021_CVPR
```
Incremental Few-Shot Instance Segmentation ,
by Ganea, Dan Andrei, Boom, Bas and Poppe, Ronald [bib]
```
Ganea_2021_CVPR
```
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? ,
by Boudiaf, Malik, Kervadec, Hoel, Masud, Ziko Imtiaz, Piantanida, Pablo, Ben Ayed, Ismail and Dolz, Jose [bib]
```
Boudiaf_2021_CVPR
```
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection ,
by Zhu, Chenchen, Chen, Fangyi, Ahmed, Uzair, Shen, Zhiqiang and Savvides, Marios [bib]
```
Zhu_2021_CVPR
```
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning ,
by Zhu, Kai, Cao, Yang, Zhai, Wei, Cheng, Jie and Zha, Zheng-Jun [bib]
```
Zhu_2021_CVPR
```
Few-Shot Classification With Feature Map Reconstruction Networks ,
by Wertheimer, Davis, Tang, Luming and Hariharan, Bharath [bib]
```
Wertheimer_2021_CVPR
```
FAPIS: A Few-Shot Anchor-Free Part-Based Instance Segmenter ,
by Nguyen, Khoi and Todorovic, Sinisa [bib]
```
Nguyen_2021_CVPR
```
Reinforced Attention for Few-Shot Learning and Beyond ,
by Hong, Jie, Fang, Pengfei, Li, Weihao, Zhang, Tong, Simon, Christian, Harandi, Mehrtash and Petersson, Lars [bib]
```
Hong_2021_CVPR
```
Dense Relation Distillation With Context-Aware Aggregation for Few-Shot Object Detection ,
by Hu, Hanzhe, Bai, Shuai, Li, Aoxue, Cui, Jinshi and Wang, Liwei [bib]
```
Hu_2021_CVPR
```
Few-Shot Open-Set Recognition by Transformation Consistency ,
by Jeong, Minki, Choi, Seokeon and Kim, Changick [bib]
```
Jeong_2021_CVPR
```
Learning Dynamic Alignment via Meta-Filter for Few-Shot Learning ,
by Xu, Chengming, Fu, Yanwei, Liu, Chen, Wang, Chengjie, Li, Jilin, Huang, Feiyue, Zhang, Li and Xue, Xiangyang [bib]
```
Xu_2021_CVPR
```
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning ,
by Rizve, Mamshad Nayeem, Khan, Salman, Khan, Fahad Shahbaz and Shah, Mubarak [bib]
```
Rizve_2021_CVPR
```
Boosting Few-Shot Learning With Adaptive Margin Loss ,
by Aoxue Li and Weiran Huang and Xu Lan and Jiashi Feng and Zhenguo Li and Liwei Wang [bib]
```
Li0LFLW20
ICCV
```
Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier ,
by Chowdhury, Arkabandhu, Jiang, Mingchao, Chaudhuri, Swarat and Jermaine, Chris [bib]
```
Chowdhury_2021_ICCV
```
Iterative Label Cleaning for Transductive and Semi-Supervised Few-Shot Learning ,
by Lazarou, Michalis, Stathaki, Tania and Avrithis, Yannis [bib]
```
Lazarou_2021_ICCV
```
On the Importance of Distractors for Few-Shot Classification ,
by Das, Rajshekhar, Wang, Yu-Xiong and Moura, Jos\'e M. F. [bib]
```
Das_2021_ICCV
TACL
```
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes ,
by Sabo, Ofer and Elazar, Yanai and Goldberg, Yoav and Dagan, Ido [bib]
```
sabo-etal-2021-revisiting
```
How Can We Know What Language Models Know ,
by Zhengbao Jiang and Frank F. Xu and Jun Araki and Graham Neubig [bib]
```
JiangXAN20
ACM Comput. Surv.
```
Generalizing from a Few Examples: A Survey on Few-shot Learning ,
by Yaqing Wang and Quanming Yao and James T. Kwok and Lionel M. Ni [bib]
```
WangYKN20
arXiv
```
AdaPrompt: Adaptive Prompt-based Finetuning for Relation Extraction ,
by Xiang Chen and Xin Xie and Ningyu Zhang and Jiahuan Yan and Shumin Deng and Chuanqi Tan and Fei Huang and Luo Si and Huajun Chen [bib]
```
abs-2104-07650
```
GPT Understands, Too ,
by Xiao Liu and Yanan Zheng and Zhengxiao Du and Ming Ding and Yujie Qian and Zhilin Yang and Jie Tang [bib]
```
abs-2103-10385
```
Prefix-Tuning: Optimizing Continuous Prompts for Generation ,
by Xiang Lisa Li and Percy Liang [bib]
```
abs-2101-00190
```
Natural Instructions: Benchmarking Generalization to New Tasks from Natural Language Instructions ,
by Swaroop Mishra and Daniel Khashabi and Chitta Baral and Hannaneh Hajishirzi [bib]
```
abs-2104-08773
```
PTR: Prompt Tuning with Rules for Text Classification ,
by Xu Han and Weilin Zhao and Ning Ding and Zhiyuan Liu and Maosong Sun [bib]
```
abs-2105-11259
```
The Power of Scale for Parameter-Efficient Prompt Tuning ,
by Brian Lester and Rami Al{-}Rfou and Noah Constant [bib]
EMNLP 2021
```
abs-2104-08691
```
Zero-Shot Controlled Generation with Encoder-Decoder Transformers ,
by Devamanyu Hazarika, Mahdi Namazifar and Dilek Hakkani-Tür [bib]
```
hazarika2021zeroshot
```
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation ,
by Kang Min Yoo and Dongju Park and Jaewook Kang and Sang{-}Woo Lee and Woomyeong Park [bib]
```
abs-2104-08826
```
Generating Datasets with Pretrained Language Models ,
by Timo Schick and Hinrich Sch{\"{u}}tze [bib]
```
abs-2104-07540
```
Neural Data Augmentation via Example Extrapolation ,
by Kenton Lee and Kelvin Guu and Luheng He and Tim Dozat and Hyung Won Chung [bib]
```
abs-2102-01335
```
Entailment as Few-Shot Learner ,
by Sinong Wang and Han Fang and Madian Khabsa and Hanzi Mao and Hao Ma [bib]
```
abs-2104-14690
```
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity ,
by Yao Lu and Max Bartolo and Alastair Moore and Sebastian Riedel and Pontus Stenetorp [bib]
```
abs-2104-08786
```
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP ,
by Jiaao Chen, Derek Tam, Colin Raffel, Mohit Bansal and Diyi Yang [bib]
```
chen2021empirical
```
Meta-tuning Language Models to Answer Prompts Better ,
by Ruiqi Zhong and Kristy Lee and Zheng Zhang and Dan Klein [bib]
```
abs-2104-04670
```
Meta-Learning with Fewer Tasks through Task Interpolation ,
by Huaxiu Yao, Linjun Zhang and Chelsea Finn [bib]
NeurIPS under-review
```
yao2021metalearning
```
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ,
by Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che and Ting Liu [bib]
ACL Findings 2021 preprint
```
hou2021learning
```
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing ,
by Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi and Graham Neubig [bib]
Prompt-based learning -- survey paper
```
liu2021pretrain
```
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification ,
by Shengding Hu and Ning Ding and Huadong Wang and Zhiyuan Liu and Juanzi Li and Maosong Sun [bib]
```
abs-2108-02035
```
Noisy Channel Language Model Prompting for Few-Shot Text Classification ,
by Sewon Min and Mike Lewis and Hannaneh Hajishirzi and Luke Zettlemoyer [bib]
```
abs-2108-04106
```
Do Prompt-Based Models Really Understand the Meaning of their Prompts? ,
by Albert Webson and Ellie Pavlick [bib]
```
webson2021promptbased
```
Prompt-Learning for Fine-Grained Entity Typing ,
by Ning Ding and Yulin Chen and Xu Han and Guangwei Xu and Pengjun Xie and Hai{-}Tao Zheng and Zhiyuan Liu and Juanzi Li and Hong{-}Gee Kim [bib]
```
abs-2108-10604
```
Want To Reduce Labeling Cost? GPT-3 Can Help ,
by Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu and Michael Zeng [bib]
EMNLP Findings 2021, adopting GPT-3 for label generation.
```
wang2021want
```
Discrete and Soft Prompting for Multilingual Models ,
by Mengjie Zhao and Hinrich Schütze [bib]
EMNLP 2021
```
zhao2021discrete
```
Few-Shot Text Generation with Pattern-Exploiting Training ,
by Timo Schick and Hinrich Sch{\"{u\textsl{}}}tze [bib]
```
abs-2012-11926
```
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field ,
by Xin Cong and Shiyao Cui and Bowen Yu and Tingwen Liu and Yubin Wang and Bin Wang [bib]
ACL 2021
```
abs-2012-02353
```
A Closer Look at Few-Shot Crosslingual Transfer: Variance, Benchmarks and Baselines ,
by Mengjie Zhao and Yi Zhu and Ehsan Shareghi and Roi Reichart and Anna Korhonen and Hinrich Sch{\"{u}}tze [bib]
ACL 2021
```
abs-2012-15682
```
Learning from Very Few Samples: A Survey ,
by Jiang Lu and Pinghua Gong and Jieping Ye and Changshui Zhang [bib]
Survey
```
abs-2009-02653
```
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models ,
by Robert L. Logan IV au2, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh and Sebastian Riedel [bib]
```
logan2021cutting
```

wutong8023 / Awesome_Few_Shot_Learning

readme

Few-shot Learning Literature

Outline

Hyperlink

ACL

EMNLP

NAACL

COLING

EACL

ICML

ICLR

NeurIPS

AAAI

IJCAI

SIGIR

KDD

CVPR

ICCV

TACL

ACM Comput. Surv.

arXiv