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title: "Who cites Model-Agnostic Meta-Learning — 632 citing papers"
description: "632 papers of the Elamur Research library cite «Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks», found in their…"
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# Who cites «Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks»

632 papers of our library cite it (of the 149,439 whose bibliographies we read) · 57 with a page here, the most cited first · [← back to «Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks»](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)

1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. **[Learning Transferable Architectures for Scalable Image Recognition](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,583 citations*
3. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
4. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
5. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
6. [**Flamingo: a Visual Language Model for Few-Shot Learning arXiv:2204.14198**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,246 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
7. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
8. **[Memory Aware Synapses: Learning what (not) to forget](https://md.research.elamur.ai/free/en/papers/aljundi-2017-memory-aware-synapses-learning-forget/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, et al.*2017 · 1,608 citations*
9. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
10. [**MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework arXiv:2308.00352**Sirui Hong, Mingchen Zhuge, Jiaqi Chen, et al.*2023 · 630 citations*](https://md.research.elamur.ai/free/en/papers/hong-2023-metagpt-meta-programming-multi/)
11. **[Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://md.research.elamur.ai/free/en/papers/wu-2019-transferable-multi-domain-state-generator/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, et al.*2019 · 628 citations*
12. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges arXiv:1907.05019**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 564 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
13. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
14. **[Meta-Learning for Low-Resource Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gu-2018-meta-learning-low-resource-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiatao Gu, Yong Wang, Yun Chen, et al.*2018 · 456 citations*
15. **[Weight Poisoning Attacks on Pretrained Models](https://md.research.elamur.ai/free/en/papers/kurita-2020-weight-poisoning-attacks-pretrained-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keita Kurita, Paul Michel, Graham Neubig*2020 · 416 citations*
16. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
17. **[A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 401 citations*
18. **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*
19. **[Universal Neural Machine Translation for Extremely Low Resource Languages](https://md.research.elamur.ai/free/en/papers/gu-2018-universal-neural-machine-translation-extremely/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiatao Gu, Hany Hassan, Jacob Devlin, et al.*2018 · 361 citations*
20. **[Few-NERD: A Few-shot Named Entity Recognition Dataset](https://md.research.elamur.ai/free/en/papers/ding-2021-few-nerd-few-shot-named/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ning Ding, Guangwei Xu, Yulin Chen, et al.*2021 · 336 citations*
21. **[AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://md.research.elamur.ai/free/en/papers/dai-2023-auggpt-leveraging-chatgpt-text-data/) [IEEE Transactions on Big Data](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-big-data/)**Haixing Dai; Zhengliang Liu; Wenxiong Liao; Xiaoke Huang; Yihan Cao; Zihao Wu; Lin Zhao; Shaochen Xu; Wei Liu; Ninghao Liu; Sheng Li; Dajiang Zhu; Hongmin Cai; Lichao Sun; Quanzheng Li; Dinggang Shen; Tianming Liu;, Xiang Li*2023 · 306 citations*
22. **[Diverse Few-Shot Text Classification with Multiple Metrics](https://md.research.elamur.ai/free/en/papers/yu-2018-diverse-few-shot-text-classification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mo Yu, Xiaoxiao Guo, Jinfeng Yi, et al.*2018 · 291 citations*
23. **[Induction Networks for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/geng-2019-induction-networks-few-shot-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruiying Geng, Binhua Li, Yongbin Li, et al.*2019 · 263 citations*
24. **[Survey of Low-Resource Machine Translation](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, et al.*2022 · 251 citations*
25. [**Learning and Evaluating General Linguistic Intelligence arXiv:1901.11373**Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, et al.*2019 · 248 citations*](https://md.research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/)
26. [**Transformers learn in-context by gradient descent arXiv:2212.07677**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 247 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
27. **[Personalizing Dialogue Agents via Meta-Learning](https://md.research.elamur.ai/free/en/papers/madotto-2019-personalizing-dialogue-agents-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, et al.*2019 · 246 citations*
28. **[Noisy Channel Language Model Prompting for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/min-2021-noisy-channel-language-model-prompting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sewon Min, Mike Lewis, Hannaneh Hajishirzi, et al.*2021 · 243 citations*
29. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
30. **[CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://md.research.elamur.ai/free/en/papers/ye-2021-crossfit-few-shot-learning-challenge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qinyuan Ye, Bill Yuchen Lin, Xiang Ren*2021 · 210 citations*
31. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
32. **[Zero-Shot Cross-Lingual Transfer with Meta Learning](https://md.research.elamur.ai/free/en/papers/nooralahzadeh-2020-zero-shot-cross-lingual-transfer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, et al.*2020 · 171 citations*
33. **[On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment](https://md.research.elamur.ai/free/en/papers/wang-2020-negative-interference-multilingual-models-findings/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zirui Wang, Zachary C. Lipton, Yulia Tsvetkov*2020 · 162 citations*
34. [**Abstraction and analogy-making in artificial intelligence**Melanie Mitchell*2021 · 161 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2021-abstraction-analogy-making-artificial-intelligence/)
35. [**A Review on Language Models as Knowledge Bases arXiv:2204.06031**Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-review-language-models-knowledge-bases/)
36. **[Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/dou-2019-investigating-meta-learning-algorithms-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos*2019 · 150 citations*
37. **[Diverse Demonstrations Improve In-context Compositional Generalization](https://md.research.elamur.ai/free/en/papers/levy-2023-diverse-demonstrations-improve-context-compositional/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Itay Levy, Ben Bogin, Jonathan Berant*2023 · 141 citations*
38. [**Transformer-Patcher: One Mistake worth One Neuron arXiv:2301.09785**Zeyu Huang, Yikang Shen, Xiaofeng Zhang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-transformer-patcher-one-mistake-worth/)
39. [**Scaling Laws for Transfer arXiv:2102.01293**Danny Hernandez, Jared Kaplan, Tom Henighan, et al.*2021 · 118 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2021-scaling-laws-transfer/)
40. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
41. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
42. [**Automated Design of Agentic Systems arXiv:2408.08435**Shengran Hu, Cong Lu, Jeff Clune*2024 · 89 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-automated-design-agentic-systems/)
43. [**OpenAgents: An Open Platform for Language Agents in the Wild arXiv:2310.10634**Tianbao Xie, Fan Zhou, Zhoujun Cheng, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-openagents-open-platform-language-agents/)
44. [**Understanding Catastrophic Forgetting in Language Models via Implicit Inference arXiv:2309.10105**Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/kotha-2023-understanding-catastrophic-forgetting-language-models/)
45. **[Continual Adaptation for Efficient Machine Communication](https://md.research.elamur.ai/free/en/papers/hawkins-2020-continual-adaptation-efficient-machine-communication/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Robert Hawkins, Minae Kwon, Dorsa Sadigh, et al.*2020 · 38 citations*
46. **[Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/guo-2021-latent-optimized-adversarial-neural-transfer/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xu Guo, Boyang Li, Han Yu, et al.*2021 · 27 citations*
47. **[Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://md.research.elamur.ai/free/en/papers/pecher-2026-automatic-combination-sample-selection-strategies/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Branislav Pecher, Ivan Srba, Maria Bielikova, et al.*2026 · 15 citations*
48. **[Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases](https://md.research.elamur.ai/free/en/papers/hu-2025-between-circuits-chomsky-pre-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michael Y. Hu, Jackson Petty, Chuan Shi, et al.*2025 · 9 citations*
49. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations*
50. **[BLUR: A Bi-Level Optimization Approach for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/reisizadeh-2026-blur-bi-level-optimization-approach/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, et al.*2026 · 4 citations*
51. [**Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents arXiv:2509.23045**Zonghan Yang, Shengjie Wang, Kelin Fu, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-kimi-dev-agentless-training/)
52. **[TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing](https://md.research.elamur.ai/free/en/papers/tian-2026-tamedit-trajectory-aware-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shiqiang Tian, Cheng Ding, Qin Chen, et al.*2026 · 1 citation*
53. **[A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German](https://md.research.elamur.ai/free/en/papers/nouri-2026-novel-synthetic-dataset-few-shot/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Shiva Banasaz Nouri, Elena Leitner, Julian Moreno-Schneider, et al.*2026*
54. **[Decoupling Generalization and Adaptation in Meta-Learning for Large Language Models](https://md.research.elamur.ai/free/en/papers/vetcha-2026-decoupling-generalization-adaptation-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nitin Vetcha, Binqian Xu, Dianbo Liu*2026*
55. **[Model-Agnostic Meta Learning for Class Imbalance Adaptation](https://md.research.elamur.ai/free/en/papers/rao-2026-model-agnostic-meta-learning-class/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanshu Rao, Guangzeng Han, Xiaolei Huang*2026*
56. **[SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation](https://md.research.elamur.ai/free/en/papers/luthra-2026-spidr-adapt-universal-speech-representation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mahi Luthra, Jiayi Shen, Maxime Poli, et al.*2026*
57. **[Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/) [Workshop on Cognitive Modeling and Computational Linguistics (CMCL)](https://md.research.elamur.ai/free/en/venues/cmcl/)**Wajdi Zaghouani*2026*

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