🔗 Who cites «CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP»

107 papers of our library cite it (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · ← back to «CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP»

1. Finetuned Language Models Are Zero-Shot LearnersarXiv:2109.01652Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.2021 · 1,954 citations
2. Multitask Prompted Training Enables Zero-Shot Task GeneralizationarXiv:2110.08207Victor Sanh, Albert Webson, Colin Raffel, et al.2021 · 1,612 citations
3. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?Conference on Empirical Methods in Natural Language Processing (EMNLP)Sewon Min, Xinxi Lyu, Ari Holtzman, et al.2022 · 1,444 citations
4. A Comprehensive Overview of Large Language ModelsACM Transactions on Intelligent Systems and TechnologyHumza Naveed, Asad Ullah Khan, Shi Qiu, et al.2023 · 1,287 citations
5. The Flan Collection: Designing Data and Methods for Effective Instruction TuningarXiv:2301.13688Shayne Longpre, Le Hou, Tu Vu, et al.2023 · 607 citations
6. True Few-Shot Learning with Language ModelsarXiv:2105.11447Ethan Perez, Douwe Kiela, Kyunghyun Cho2021 · 412 citations
7. UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Tianbao Xie, Chen Henry Wu, Peng Shi, et al.2022 · 407 citations
8. MAmmoTH: Building Math Generalist Models through Hybrid Instruction TuningarXiv:2309.05653Xiang Yue, Xingwei Qu, Ge Zhang, et al.2023 · 295 citations
9. OpenPrompt: An Open-source Framework for Prompt-learningAnnual Meeting of the Association for Computational Linguistics (ACL)Ning Ding, Shengding Hu, Weilin Zhao, et al.2022 · 279 citations
10. Surface Form Competition: Why the Highest Probability Answer Isn't Always RightConference on Empirical Methods in Natural Language Processing (EMNLP)Ari Holtzman, Peter West, Vered Shwartz, et al.2021 · 277 citations
11. OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationarXiv:2212.12017Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.2022 · 244 citations
12. Generating Training Data with Language Models: Towards Zero-Shot Language UnderstandingarXiv:2202.04538Yu Meng, Jiaxin Huang, Yu Zhang, et al.2022 · 210 citations
13. Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.2024 · 207 citations
14. Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt CollectionsarXiv:2104.04670Ruiqi Zhong, Kristy Lee, Zheng Zhang, et al.2021 · 195 citations
15. From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction TuningarXiv:2308.12032Ming Li, Yong Zhang, Zhitao Li, et al.2023 · 161 citations
16. Chain of Hindsight Aligns Language Models with FeedbackarXiv:2302.02676Hao Liu, Carmelo Sferrazza, Pieter Abbeel2023 · 126 citations
17. Ground-Truth Labels Matter: A Deeper Look into Input-Label DemonstrationsConference on Empirical Methods in Natural Language Processing (EMNLP)Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.2022 · 114 citations
18. LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA CompositionarXiv:2307.13269Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.2023 · 108 citations
19. MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical ReasoningarXiv:2310.03731Ke Wang, Houxing Ren, Aojun Zhou, et al.2023 · 99 citations
20. True Few-Shot Learning with Prompts–-A Real-World PerspectiveTransactions of the Association for Computational Linguistics (TACL)Timo Schick, Hinrich Schütze2022 · 96 citations
21. BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual TransferConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Akari Asai, Sneha Kudugunta, Xinyan Yu, et al.2024 · 86 citations
22. Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and OpportunitiesarXiv:2408.07666Enneng Yang, Li Shen, Guibing Guo, et al.2024 · 79 citations
23. Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language ModelsarXiv:2305.14705Sheng Shen, Le Hou, Yanqi Zhou, et al.2023 · 70 citations
24. Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use CasesarXiv:2303.14742Yunjie Ji, Yong Deng, Yan Gong, et al.2023 · 62 citations
25. Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-TuningarXiv:2402.00530Ming Li, Yong Zhang, Shwai He, et al.2024 · 60 citations
26. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction TuningarXiv:2304.07995Qian Liu, Fan Zhou, Zhengbao Jiang, et al.2023 · 24 citations
27. From Selection to Generation: A Survey of LLM-based Active LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, et al.2025 · 22 citations
28. Factual Dialogue Summarization via Learning from Large Language ModelsInternational Conference on Computational Linguistics (COLING)Rongxin Zhu, Jey Han Lau, Jianzhong Qi2025 · 10 citations

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