🔗 Who cites «Improving and Simplifying Pattern Exploiting Training»

73 papers of our library cite it (of the 141,840 whose bibliographies we read) · 12 with a page here, the most cited first · ← back to «Improving and Simplifying Pattern Exploiting Training»

1. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language ProcessingPengfei Liu, Weizhe Yuan, Jinlan Fu, et al.2021 · 5,396 citations
2. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningHaokun Liu, Derek Tam, Mohammed Muqeeth, et al.2022 · 736 citations
3. True Few-Shot Learning with Language ModelsEthan Perez, Douwe Kiela, Kyunghyun Cho2021 · 404 citations
4. Do Prompt-Based Models Really Understand the Meaning of Their Prompts?Albert Webson, Ellie Pavlick2022 · 398 citations
5. RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningMingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.2022 · 328 citations
6. Generating Datasets with Pretrained Language ModelsTimo Schick, Hinrich Schutze2021 · 250 citations
7. Noisy Channel Language Model Prompting for Few-Shot Text ClassificationSewon Min, Mike Lewis, Hannaneh Hajishirzi, et al.2021 · 238 citations
8. Reframing Instructional Prompts to GPTk's LanguageSwaroop Mishra, Daniel Khashabi, Chitta Baral, et al.2022 · 236 citations
9. Generating Training Data with Language Models: Towards Zero-Shot Language UnderstandingYu Meng, Jiaxin Huang, Yu Zhang, et al.2022 · 207 citations
10. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language ModelsArchiki Prasad, Peter Hase, Xiang Zhou, et al.2023 · 182 citations
11. Label Verbalization and Entailment for Effective Zero and Few-Shot Relation ExtractionOscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, et al.2021 · 167 citations
12. Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and EvaluationMarius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.2023 · 147 citations

61 more papers of our library cite it; they have no page here yet.

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