Few-shot Learning Literature

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This page categorizes the literature by the Published Venue.

Outline

Hyperlink

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]
```
karimi-mahabadi-etal-2022-prompt
```
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning ,
by Das, Sarkar Snigdha Sarathi and Katiyar, Arzoo and Passonneau, Rebecca and Zhang, Rui [bib]
```
das-etal-2022-container
```
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]
```
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]
```
jin-etal-2022-good
```
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
```
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]
```
zhou-etal-2022-flipda
```
Prototypical Verbalizer for Prompt-based Few-shot Tuning ,
by Cui, Ganqu and Hu, Shengding and Ding, Ning and Huang, Longtao and Liu, Zhiyuan [bib]
```
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]
```
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]
```
muller-etal-2022-shot
```
PPT: Pre-trained Prompt Tuning for Few-shot Learning ,
by Gu, Yuxian and Han, Xu and Liu, Zhiyuan and Huang, Minlie [bib]
```
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]
```
tabasi-etal-2022-exploiting
```
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]
```
ShenWQLHB21
```
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]
```
zhao-etal-2021-closer
```
Few-Shot Question Answering by Pretraining Span Selection ,
by Ram, Ori and Kirstain, Yuval and Berant, Jonathan and Globerson, Amir and Levy, Omer [bib]
```
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]
```
ding-etal-2021-nerd
```
Making Pre-trained Language Models Better Few-shot Learners ,
by Gao, Tianyu and Fisch, Adam and Chen, Danqi [bib]
```
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]
```
ohashi-etal-2021-distinct
```
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]
```
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]
```
hu-etal-2021-multi-label
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Lexicon Learning for Few Shot Sequence Modeling ,
by Akyurek, Ekin and Andreas, Jacob [bib]
```
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]
```
tong-etal-2021-learning
```
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]
```
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]
```
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]
```
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]
```
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
```
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification ,
by Zhi{-}Xiu Ye and Zhen{-}Hua Ling [bib]
MLMAN
```
YeL19
```

EMNLP

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
```
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]
```
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]
```
mi-etal-2021-self
```
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
```
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]
```
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]
```
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.
```
micheli-fleuret-2021-language
```
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]
```
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
```
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]
```
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]
```
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]
```
pasunuru-etal-2021-continual
```
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]
```
huang-etal-2021-shot
```
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.
```
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]
```
chada-natarajan-2021-fewshotqa
```
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning ,
by Utama, Prasetya and Moosavi, Nafise Sadat and Sanh, Victor and Gurevych, Iryna [bib]
```
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]
```
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]
```
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]
```
lakhotia-etal-2021-fid
```
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]
```
guibon-etal-2021-shot
```
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]
```
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]
```
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]
```
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]
```
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]
```
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]
```
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]
```
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]
```
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]
```
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]
```
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

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]
```
ahuja-etal-2022-economics
```
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]
```
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]
```
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]
```
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]
```
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]
```
wiegreffe-etal-2022-reframing
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Few-Shot Document-Level Relation Extraction ,
by Popovic, Nicholas and F{"a}rber, Michael [bib]
```
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]
```
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]
```
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]
```
qin-eisner-2021-learning
```
Factual Probing Is [MASK]: Learning vs. Learning to Recall ,
by Zhong, Zexuan and Friedman, Dan and Chen, Danqi [bib]
```
zhong-etal-2021-factual
```
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners ,
by Schick, Timo and Sch{"u}tze, Hinrich [bib]
```
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
```
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
```
ConVEx: Data-Efficient and Few-Shot Slot Labeling ,
by Henderson, Matthew and Vuli{'c}, Ivan [bib]
```
henderson-vulic-2021-convex
```
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

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
```
Channel Importance Matters in Few-Shot Image Classification ,
by Luo, Xu, Xu, Jing and Xu, Zenglin [bib]
```
pmlr-v162-luo22c
```
Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold ,
by Sharma, Sugandha, Chandra, Sarthak and Fiete, Ila [bib]
```
pmlr-v162-sharma22b
```
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
```
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning ,
by Zhmoginov, Andrey, Sandler, Mark and Vladymyrov, Maksym [bib]
```
pmlr-v162-zhmoginov22a
```
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
```
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification ,
by Lee, Dong Hoon and Chung, Sae-Young [bib]
```
pmlr-v139-lee21d
```
Large-Scale Meta-Learning with Continual Trajectory Shifting ,
by Shin, Jaewoong, Lee, Hae Beom, Gong, Boqing and Hwang, Sung Ju [bib]
```
pmlr-v139-shin21a
```
Few-shot Language Coordination by Modeling Theory of Mind ,
by Zhu, Hao, Neubig, Graham and Bisk, Yonatan [bib]
```
pmlr-v139-zhu21d
```
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
```
Few-Shot Neural Architecture Search ,
by Zhao, Yiyang, Wang, Linnan, Tian, Yuandong, Fonseca, Rodrigo and Guo, Tian [bib]
```
pmlr-v139-zhao21d
```
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]
```
pmlr-v139-cui21a
```
How Important is the Train-Validation Split in Meta-Learning? ,
by Bai, Yu, Chen, Minshuo, Zhou, Pan, Zhao, Tuo, Lee, Jason, Kakade, Sham, Wang, Huan and Xiong, Caiming [bib]
```
pmlr-v139-bai21a
```
Few-Shot Conformal Prediction with Auxiliary Tasks ,
by Fisch, Adam, Schuster, Tal, Jaakkola, Tommi and Barzilay, Dr.Regina [bib]
```
pmlr-v139-fisch21a
```
A Distribution-dependent Analysis of Meta Learning ,
by Konobeev, Mikhail, Kuzborskij, Ilja and Szepesvari, Csaba [bib]
```
pmlr-v139-konobeev21a
```
Data Augmentation for Meta-Learning ,
by Ni, Renkun, Goldblum, Micah, Sharaf, Amr, Kong, Kezhi and Goldstein, Tom [bib]
```
pmlr-v139-ni21a
```
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation ,
by Wang, Haoxiang, Zhao, Han and Li, Bo [bib]
```
pmlr-v139-wang21ad
```
CURI: A Benchmark for Productive Concept Learning Under Uncertainty ,
by Vedantam, Ramakrishna, Szlam, Arthur, Nickel, Maximillian, Morcos, Ari and Lake, Brenden M [bib]
```
pmlr-v139-vedantam21a
```
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning ,
by Saunshi, Nikunj, Gupta, Arushi and Hu, Wei [bib]
```
pmlr-v139-saunshi21a
```
Memory Efficient Online Meta Learning ,
by Acar, Durmus Alp Emre, Zhu, Ruizhao and Saligrama, Venkatesh [bib]
```
pmlr-v139-acar21b
```
Addressing Catastrophic Forgetting in Few-Shot Problems ,
by Yap, Pauching, Ritter, Hippolyt and Barber, David [bib]
```
pmlr-v139-yap21a
```
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning ,
by Achituve, Idan, Navon, Aviv, Yemini, Yochai, Chechik, Gal and Fetaya, Ethan [bib]
```
pmlr-v139-achituve21a
```
TaskNorm: Rethinking Batch Normalization for Meta-Learning ,
by Bronskill, John, Gordon, Jonathan, Requeima, James, Nowozin, Sebastian and Turner, Richard [bib]
```
pmlr-v119-bronskill20a
```
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks ,
by Goldblum, Micah, Reich, Steven, Fowl, Liam, Ni, Renkun, Cherepanova, Valeriia and Goldstein, Tom [bib]
```
pmlr-v119-goldblum20a
```
Meta-Learning with Shared Amortized Variational Inference ,
by Iakovleva, Ekaterina, Verbeek, Jakob and Alahari, Karteek [bib]
```
pmlr-v119-iakovleva20a
```
Meta Variance Transfer: Learning to Augment from the Others ,
by Park, Seong-Jin, Han, Seungju, Baek, Ji-Won, Kim, Insoo, Song, Juhwan, Lee, Hae Beom, Han, Jae-Joon and Hwang, Sung Ju [bib]
```
pmlr-v119-park20b
```
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs ,
by Qu, Meng, Gao, Tianyu, Xhonneux, Louis-Pascal and Tang, Jian [bib]
```
pmlr-v119-qu20a
```
Few-shot Domain Adaptation by Causal Mechanism Transfer ,
by Teshima, Takeshi, Sato, Issei and Sugiyama, Masashi [bib]
```
pmlr-v119-teshima20a
```
Frustratingly Simple Few-Shot Object Detection ,
by Wang, Xin, Huang, Thomas, Gonzalez, Joseph, Darrell, Trevor and Yu, Fisher [bib]
```
pmlr-v119-wang20j
```
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning ,
by Yoon, Sung Whan, Kim, Do-Yeon, Seo, Jun and Moon, Jaekyun [bib]
```
pmlr-v119-yoon20b
```
Infinite Mixture Prototypes for Few-shot Learning ,
by Allen, Kelsey, Shelhamer, Evan, Shin, Hanul and Tenenbaum, Joshua [bib]
```
pmlr-v97-allen19b
```
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning ,
by Li, Huaiyu, Dong, Weiming, Mei, Xing, Ma, Chongyang, Huang, Feiyue and Hu, Bao-Gang [bib]
```
pmlr-v97-li19c
```
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning ,
by Yoon, Sung Whan, Seo, Jun and Moon, Jaekyun [bib]
```
pmlr-v97-yoon19a
```
Hierarchically Structured Meta-learning ,
by Yao, Huaxiu, Wei, Ying, Huang, Junzhou and Li, Zhenhui [bib]
```
pmlr-v97-yao19b
```
Fast Context Adaptation via Meta-Learning ,
by Zintgraf, Luisa, Shiarli, Kyriacos, Kurin, Vitaly, Hofmann, Katja and Whiteson, Shimon [bib]
```
pmlr-v97-zintgraf19a
```
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning ,
by Zhao, Bo, Sun, Xinwei, Fu, Yanwei, Yao, Yuan and Wang, Yizhou [bib]
```
pmlr-v80-zhao18c
```
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory ,
by Amit, Ron and Meir, Ron [bib]
```
pmlr-v80-amit18a
```
Bilevel Programming for Hyperparameter Optimization and Meta-Learning ,
by Franceschi, Luca, Frasconi, Paolo, Salzo, Saverio, Grazzi, Riccardo and Pontil, Massimiliano [bib]
```
pmlr-v80-franceschi18a
```
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace ,
by Lee, Yoonho and Choi, Seungjin [bib]
```
pmlr-v80-lee18a
```
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ,
by Chelsea Finn, Pieter Abbeel and Sergey Levine [bib]
```
pmlr-v70-finn17a
```
Meta Networks ,
by Tsendsuren Munkhdalai and Hong Yu [bib]
```
pmlr-v70-munkhdalai17a
```

ICLR

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners ,
by Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang and Huajun Chen [bib]
```
zhang2022differentiable
```
Exploring the Limits of Large Scale Pre-training ,
by Samira Abnar, Mostafa Dehghani, Behnam Neyshabur and Hanie Sedghi [bib]
```
abnar2022exploring
```
Subspace Regularizers for Few-Shot Class Incremental Learning ,
by Afra Feyza Aky{"u}rek, Ekin Aky{"u}rek, Derry Wijaya and Jacob Andreas [bib]
```
akyurek2022subspace
```
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning ,
by Cat Phuoc Le, Juncheng Dong, Mohammadreza Soltani and Vahid Tarokh [bib]
```
le2022task
```
On the Importance of Firth Bias Reduction in Few-Shot Classification ,
by Saba Ghaffari, Ehsan Saleh, David Forsyth and Yu-Xiong Wang [bib]
```
ghaffari2022on
```
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification ,
by Zhengdong Hu, Yifan Sun and Yi Yang [bib]
```
hu2022switch
```
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 ,
by Chengwei Qin and Shafiq Joty [bib]
```
qin2022lfpt
```
Hierarchical Few-Shot Imitation with Skill Transition Models ,
by Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel and Michael Laskin [bib]
```
hakhamaneshi2022hierarchical
```
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning ,
by Debasmit Das, Sungrack Yun and Fatih Porikli [bib]
```
das2022confess
```
Hierarchical Variational Memory for Few-shot Learning Across Domains ,
by Yingjun Du, Xiantong Zhen, Ling Shao and Cees G. M. Snoek [bib]
```
du2022hierarchical
```
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification ,
by Bing Su and Ji-Rong Wen [bib]
```
su2022temporal
```
Generalizing Few-Shot NAS with Gradient Matching ,
by Shoukang Hu, Ruochen Wang, Lanqing HONG, Zhenguo Li, Cho-Jui Hsieh and Jiashi Feng [bib]
```
hu2022generalizing
```
Few-shot Learning via Dirichlet Tessellation Ensemble ,
by Chunwei Ma, Ziyun Huang, Mingchen Gao and Jinhui Xu [bib]
```
ma2022fewshot
```
How to Train Your MAML to Excel in Few-Shot Classification ,
by Han-Jia Ye and Wei-Lun Chao [bib]
```
ye2022how
```
Free Lunch for Few-shot Learning: Distribution Calibration ,
by Shuo Yang, Lu Liu and Min Xu [bib]
```
yang2021free
```
Self-training For Few-shot Transfer Across Extreme Task Differences ,
by Cheng Perng Phoo and Bharath Hariharan [bib]
```
phoo2021selftraining
```
Wandering within a world: Online contextualized few-shot learning ,
by Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer and Richard Zemel [bib]
```
ren2021wandering
```
Few-Shot Learning via Learning the Representation, Provably ,
by Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee and Qi Lei [bib]
```
du2021fewshot
```
A Universal Representation Transformer Layer for Few-Shot Image Classification ,
by Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang and Hugo Larochelle [bib]
```
liu2021a
```
Revisiting Few-sample \BERT\ Fine-tuning ,
by Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger and Yoav Artzi [bib]
```
zhang2021revisiting
```
Concept Learners for Few-Shot Learning ,
by Kaidi Cao, Maria Brbic and Jure Leskovec [bib]
```
cao2021concept
```
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in \NLP\ Using Fewer Parameters & Less Data ,
by Jonathan Pilault, Amine El hattami and Christopher Pal [bib]
```
pilault2021conditionally
```
Incremental few-shot learning via vector quantization in deep embedded space ,
by Kuilin Chen and Chi-Guhn Lee [bib]
```
chen2021incremental
```
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning ,
by Namyeong Kwon, Hwidong Na, Gabriel Huang and Simon Lacoste-Julien [bib]
```
kwon2021repurposing
```
\MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning ,
by Nanyi Fei, Zhiwu Lu, Tao Xiang and Songfang Huang [bib]
```
fei2021melr
```
Disentangling 3D Prototypical Networks for Few-Shot Concept Learning ,
by Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam W Harley and Katerina Fragkiadaki [bib]
```
prabhudesai2021disentangling
```
Attentional Constellation Nets for Few-Shot Learning ,
by Weijian Xu, yifan xu, Huaijin Wang and Zhuowen Tu [bib]
```
xu2021attentional
```
\BOIL: Towards Representation Change for Few-shot Learning ,
by Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim and Se-Young Yun [bib]
```
oh2021boil
```
Theoretical bounds on estimation error for meta-learning ,
by James Lucas, Mengye Ren, Irene Raissa KAMENI KAMENI, Toniann Pitassi and Richard Zemel [bib]
```
lucas2021theoretical
```
Meta-Learning of Structured Task Distributions in Humans and Machines ,
by Sreejan Kumar, Ishita Dasgupta, Jonathan Cohen, Nathaniel Daw and Thomas Griffiths [bib]
```
kumar2021metalearning
```
Automated Relational Meta-learning ,
by Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li and Zhenhui Li [bib]
```
Yao2020Automated
```
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples ,
by Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol and Hugo Larochelle [bib]
```
Triantafillou2020Meta-Dataset
```
Towards Fast Adaptation of Neural Architectures with Meta Learning ,
by Dongze Lian, Yin Zheng, Yintao Xu, Yanxiong Lu, Leyu Lin, Peilin Zhao, Junzhou Huang and Shenghua Gao [bib]
```
Lian2020Towards
```
Bayesian Meta Sampling for Fast Uncertainty Adaptation ,
by Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang and Changyou Chen [bib]
```
Wang2020Bayesian
```
Meta-Learning without Memorization ,
by Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine and Chelsea Finn [bib]
```
Yin2020Meta-Learning
```
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization ,
by Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter and Christian Daniel [bib]
```
Volpp2020Meta-Learning
```
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks ,
by Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang and Sung Ju Hwang [bib]
```
Lee2020Learning
```
Few-shot Text Classification with Distributional Signatures ,
by Yujia Bao, Menghua Wu, Shiyu Chang and Regina Barzilay [bib]
```
Bao2020Few-shot
```
Disentangling Factors of Variations Using Few Labels ,
by Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf and Olivier Bachem [bib]
```
Locatello2020Disentangling
```
FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES BASED ON GRAPH SPECTRAL MEASURES ,
by Jatin Chauhan, Deepak Nathani and Manohar Kaul [bib]
```
Chauhan2020FEW-SHOT
```
A Theoretical Analysis of the Number of Shots in Few-Shot Learning ,
by Tianshi Cao, Marc T Law and Sanja Fidler [bib]
```
Cao2020A
```
A Baseline for Few-Shot Image Classification ,
by Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran and Stefano Soatto [bib]
```
Dhillon2020A
```
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation ,
by Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang and Ming-Hsuan Yang [bib]
```
Tseng2020Cross-Domain
```
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module ,
by Tiago Ramalho and Marta Garnelo [bib]
```
ramalho2018adaptive
```
A Closer Look at Few-shot Classification ,
by Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang and Jia-Bin Huang [bib]
```
chen2018a
```
LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING ,
by Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sungju Hwang and Yi Yang [bib]
```
liu2018learning
```
Transferring Knowledge across Learning Processes ,
by Sebastian Flennerhag, Pablo Garcia Moreno, Neil Lawrence and Andreas Damianou [bib]
```
flennerhag2018transferring
```
Meta-Learning Probabilistic Inference for Prediction ,
by Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin and Richard Turner [bib]
```
gordon2018metalearning
```
Learning to Learn with Conditional Class Dependencies ,
by Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados and Stan Matwin [bib]
```
jiang2018learning
```
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions ,
by Scott Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo Rezende, Oriol Vinyals and Nando de Freitas [bib]
```
reed2018fewshot
```
Few-Shot Learning with Graph Neural Networks ,
by Victor Garcia Satorras and Joan Bruna Estrach [bib]
```
garcia2018fewshot
```
Meta-Learning for Semi-Supervised Few-Shot Classification ,
by Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle and Richard S. Zemel [bib]
```
ren2018metalearning
```
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm ,
by Chelsea Finn and Sergey Levine [bib]
```
finn2018metalearning
```
META LEARNING SHARED HIERARCHIES ,
by Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel and John Schulman [bib]
```
frans2018meta
```
Optimization as a Model for Few-Shot Learning ,
by Sachin Ravi and Hugo Larochelle [bib]
```
RaviL17
```

NeurIPS

Realistic evaluation of transductive few-shot learning ,
by Olivier Veilleux and Malik Boudiaf and Pablo Piantanida and Ismail Ben Ayed [bib]
```
VeilleuxBPA21
```
Re-ranking for image retrieval and transductive few-shot classification ,
by Xi Shen and Yang Xiao and Shell Xu Hu and Othman Sbai and Mathieu Aubry [bib]
```
ShenXHSA21
```
True Few-Shot Learning with Language Models ,
by Ethan Perez and Douwe Kiela and Kyunghyun Cho [bib]
```
PerezKC21
```
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation ,
by Jixuan Wang and Kuan{-}Chieh Wang and Frank Rudzicz and Michael Brudno [bib]
```
WangWRB21
```
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation ,
by Abhishek Sinha and Jiaming Song and Chenlin Meng and Stefano Ermon [bib]
```
SinhaSME21
```
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation ,
by Haoang Chi and Feng Liu and Wenjing Yang and Long Lan and Tongliang Liu and Bo Han and William K. Cheung and James T. Kwok [bib]
```
ChiLYLLHCK21
```
The Role of Global Labels in Few-Shot Classification and How to Infer Them ,
by Ruohan Wang and Massimiliano Pontil and Carlo Ciliberto [bib]
```
WangPC21
```
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data ,
by Ashraful Islam and Chun{-}Fu (Richard) Chen and Rameswar Panda and Leonid Karlinsky and Rog{'{e}}rio Feris and Richard J. Radke [bib]
```
IslamCPKFR21
```
Learning to Learn Dense Gaussian Processes for Few-Shot Learning ,
by Ze Wang and Zichen Miao and Xiantong Zhen and Qiang Qiu [bib]
```
WangMZQ21
```
Rectifying the Shortcut Learning of Background for Few-Shot Learning ,
by Xu Luo and Longhui Wei and Liangjian Wen and Jinrong Yang and Lingxi Xie and Zenglin Xu and Qi Tian [bib]
```
LuoWWYXXT21
```
FLEX: Unifying Evaluation for Few-Shot NLP ,
by Jonathan Bragg and Arman Cohan and Kyle Lo and Iz Beltagy [bib]
```
BraggCLB21
```
Multimodal Few-Shot Learning with Frozen Language Models ,
by Maria Tsimpoukelli and Jacob Menick and Serkan Cabi and S. M. Ali Eslami and Oriol Vinyals and Felix Hill [bib]
```
TsimpoukelliMCE21
```
On Episodes, Prototypical Networks, and Few-Shot Learning ,
by Steinar Laenen and Luca Bertinetto [bib]
```
LaenenB21
```
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples ,
by Duong H. Le and Khoi Duc Nguyen and Khoi Nguyen and Quoc{-}Huy Tran and Rang Nguyen and Binh{-}Son Hua [bib]
```
LeNNTNH21
```
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima ,
by Guangyuan Shi and Jiaxin Chen and Wenlong Zhang and Li{-}Ming Zhan and Xiao{-}Ming Wu [bib]
```
ShiCZZW21
```
Few-Shot Learning Evaluation in Natural Language Understanding ,
by Subhabrata Mukherjee and Xiaodong Liu and Guoqing Zheng and Saghar Hosseini and Saghar Hosseini and Hao Cheng and Ge Yang and Christopher Meek and Ahmed Hassan Awadallah and Jianfeng Gao [bib]
```
MukherjeeLZHH0Y21
```
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction ,
by Jinheon Baek and Dong Bok Lee and Sung Ju Hwang [bib]
```
BaekLH20
```
Information Maximization for Few-Shot Learning ,
by Malik Boudiaf and Imtiaz Masud Ziko and J{'{e}}r{^{o}}me Rony and Jose Dolz and Pablo Piantanida and Ismail Ben Ayed [bib]
```
BoudiafZRDPA20
```
Interventional Few-Shot Learning ,
by Zhongqi Yue and Hanwang Zhang and Qianru Sun and Xian{-}Sheng Hua [bib]
```
YueZS020
```
Restoring Negative Information in Few-Shot Object Detection ,
by Yukuan Yang and Fangyun Wei and Miaojing Shi and Guoqi Li [bib]
```
YangWSL20
```
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification ,
by Taewon Jeong and Heeyoung Kim [bib]
```
JeongK20
```
Few-shot Image Generation with Elastic Weight Consolidation ,
by Yijun Li and Richard Zhang and Jingwan Lu and Eli Shechtman [bib]
```
Li0LS20
```
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding ,
by Lin Lan and Pinghui Wang and Xuefeng Du and Kaikai Song and Jing Tao and Xiaohong Guan [bib]
```
LanWDSTG20
```
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning ,
by Youngsung Kim and Jinwoo Shin and Eunho Yang and Sung Ju Hwang [bib]
```
KimSYH20
```
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach ,
by Micah Goldblum and Liam Fowl and Tom Goldstein [bib]
```
GoldblumFG20
```
Uncertainty-aware Self-training for Few-shot Text Classification ,
by Subhabrata Mukherjee and Ahmed Hassan Awadallah [bib]
```
MukherjeeA20
```
A Closer Look at the Training Strategy for Modern Meta-Learning ,
by Jiaxin Chen and Xiao{-}Ming Wu and Yanke Li and Qimai Li and Li{-}Ming Zhan and Fu{-}Lai Chung [bib]
```
ChenWLLZC20
```
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning ,
by Giulia Denevi and Massimiliano Pontil and Carlo Ciliberto [bib]
```
DeneviPC20
```
Structured Prediction for Conditional Meta-Learning ,
by Ruohan Wang and Yiannis Demiris and Carlo Ciliberto [bib]
```
WangDC20
```
Balanced Meta-Softmax for Long-Tailed Visual Recognition ,
by Jiawei Ren and Cunjun Yu and Shunan Sheng and Xiao Ma and Haiyu Zhao and Shuai Yi and Hongsheng Li [bib]
```
RenYSMZYL20
```
Meta-Learning Requires Meta-Augmentation ,
by Janarthanan Rajendran and Alexander Irpan and Eric Jang [bib]
```
RajendranIJ20
```
Meta-learning from Tasks with Heterogeneous Attribute Spaces ,
by Tomoharu Iwata and Atsutoshi Kumagai [bib]
```
IwataK20
```
Online Structured Meta-learning ,
by Huaxiu Yao and Yingbo Zhou and Mehrdad Mahdavi and Zhenhui Li and Richard Socher and Caiming Xiong [bib]
```
YaoZMLSX20
```
Modeling and Optimization Trade-off in Meta-learning ,
by Katelyn Gao and Ozan Sener [bib]
```
GaoS20
```
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters ,
by Kaiyi Ji and Jason D. Lee and Yingbin Liang and H. Vincent Poor [bib]
```
JiLLP20
```
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning ,
by Xiaohan Chen and Zhangyang Wang and Siyu Tang and Krikamol Muandet [bib]
```
ChenWTM20
```
Continuous Meta-Learning without Tasks ,
by James Harrison and Apoorva Sharma and Chelsea Finn and Marco Pavone [bib]
```
HarrisonSFP20
```
Task-Robust Model-Agnostic Meta-Learning ,
by Liam Collins and Aryan Mokhtari and Sanjay Shakkottai [bib]
```
CollinsMS20
```
Meta-Learning with Adaptive Hyperparameters ,
by Sungyong Baik and Myungsub Choi and Janghoon Choi and Heewon Kim and Kyoung Mu Lee [bib]
```
BaikCCKL20
```
Probabilistic Active Meta-Learning ,
by Jean Kaddour and Steind{'{o}}r S{\ae}mundsson and Marc Peter Deisenroth [bib]
```
KaddourSD20
```
Learning to Learn Variational Semantic Memory ,
by Xiantong Zhen and Ying{-}Jun Du and Huan Xiong and Qiang Qiu and Cees Snoek and Ling Shao [bib]
```
ZhenDXQS020
```
Language Models are Few-Shot Learners ,
by Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert{-}Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei [bib]
```
BrownMRSKDNSSAA20
```
Cross Attention Network for Few-shot Classification ,
by Ruibing Hou and Hong Chang and Bingpeng Ma and Shiguang Shan and Xilin Chen [bib]
```
HouCMSC19
```
Adaptive Cross-Modal Few-shot Learning ,
by Chen Xing and Negar Rostamzadeh and Boris N. Oreshkin and Pedro O. Pinheiro [bib]
```
XingROP19
```
Unsupervised Meta-Learning for Few-Shot Image Classification ,
by Siavash Khodadadeh and Ladislau B{"{o}}l{"{o}}ni and Mubarak Shah [bib]
```
KhodadadehBS19
```
Learning to Self-Train for Semi-Supervised Few-Shot Classification ,
by Xinzhe Li and Qianru Sun and Yaoyao Liu and Qin Zhou and Shibao Zheng and Tat{-}Seng Chua and Bernt Schiele [bib]
```
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
```

Name		Name	Last commit message	Last commit date
Latest commit History 142 Commits
fsl4all		fsl4all
fsl4cv		fsl4cv
fsl4nlp		fsl4nlp
scripts		scripts
README.md		README.md
bibtex.bib		bibtex.bib

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