🔗 Who cites «Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections»

96 papers of our library cite it (of the 149,439 whose bibliographies we read) · 29 with a page here, the most cited first · ← back to «Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections»

1. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language ProcessingACM Computing SurveysPengfei Liu, Weizhe Yuan, Jinlan Fu, et al.2021 · 5,423 citations
2. QLoRA: Efficient Finetuning of Quantized LLMsConference on Neural Information Processing Systems (NeurIPS)Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.2023 · 2,929 citations
3. Finetuned Language Models Are Zero-Shot LearnersarXiv:2109.01652Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.2021 · 1,954 citations
4. Multitask Prompted Training Enables Zero-Shot Task GeneralizationarXiv:2110.08207Victor Sanh, Albert Webson, Colin Raffel, et al.2021 · 1,612 citations
5. Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A SurveyACM Computing SurveysBonan Min, Hayley Ross, Elior Sulem, et al.2021 · 1,556 citations
6. GPT-NeoX-20B: An Open-Source Autoregressive Language ModelBigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language ModelsSidney Black, Stella Biderman, Eric Hallahan, et al.2022 · 831 citations
7. Crosslingual Generalization through Multitask FinetuningarXiv:2211.01786Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.2022 · 703 citations
8. Instruction Tuning with GPT-4arXiv:2304.03277Baolin Peng, Chunyuan Li, Pengcheng He, et al.2023 · 662 citations
9. Instruction-Following Evaluation for Large Language ModelsarXiv:2311.07911Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, et al.2023 · 615 citations
10. PPT: Pre-trained Prompt Tuning for Few-shot LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yuxian Gu, Xu Han, Zhiyuan Liu, et al.2022 · 471 citations
11. 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
12. Editing Models with Task ArithmeticarXiv:2212.04089Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.2022 · 402 citations
13. RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.2022 · 338 citations
14. ZeroGen: Efficient Zero-shot Learning via Dataset GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Jiacheng Ye, Jiahui Gao, Qintong Li, et al.2022 · 245 citations
15. 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
16. Generating Training Data with Language Models: Towards Zero-Shot Language UnderstandingarXiv:2202.04538Yu Meng, Jiaxin Huang, Yu Zhang, et al.2022 · 210 citations
17. CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPConference on Empirical Methods in Natural Language Processing (EMNLP)Qinyuan Ye, Bill Yuchen Lin, Xiang Ren2021 · 210 citations
18. One Embedder, Any Task: Instruction-Finetuned Text EmbeddingsarXiv:2212.09741Hongjin Su, Weijia Shi, Jungo Kasai, et al.2022 · 193 citations
19. GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionConference on Neural Information Processing Systems (NeurIPS)Rui Yang, Lin Song, Yanwei Li, et al.2023 · 147 citations
20. LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mihir Parmar, Nisarg Patel, Neeraj Varshney, et al.2024 · 93 citations
21. Large Language Model Instruction Following: A Survey of Progresses and ChallengesComputational LinguisticsRenze Lou, Kai Zhang, Wenpeng Yin2024 · 83 citations
22. DOC: Improving Long Story Coherence With Detailed Outline ControlAnnual Meeting of the Association for Computational Linguistics (ACL)Kevin Yang, Dan Klein, Nanyun Peng, et al.2023 · 83 citations
23. Learning by Distilling ContextarXiv:2209.15189Charlie Snell, Dan Klein, Ruiqi Zhong2022 · 67 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. CFBench: A Comprehensive Constraints-Following Benchmark for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Tao Zhang, ChengLIn Zhu, Yanjun Shen, et al.2025 · 30 citations
26. Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learningConference on Computational Natural Language Learning (CoNLL)Lucas Weber, Elia Bruni, Dieuwke Hupkes2023 · 29 citations
27. The ICL Consistency TestarXiv:2312.04945Lucas Weber, Elia Bruni, Dieuwke Hupkes2023 · 12 citations
28. EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Tao Zou, Xinghua Zhang, Haiyang Yu, et al.2025 · 5 citations
29. Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战)Acta Automatica Sinica (自动化学报)Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.2023

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