🧑‍🔬 Who cites Rakesh Menon

80 citations of their works from 80 papers of our library (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · ← Rakesh Menon

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. Finetuned Language Models Are Zero-Shot LearnersarXiv:2109.01652Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.2021 · 1,954 citations
3. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.2022 · 754 citations
4. Atlas: Few-shot Learning with Retrieval Augmented Language ModelsarXiv:2208.03299Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.2022 · 673 citations
5. True Few-Shot Learning with Language ModelsarXiv:2105.11447Ethan Perez, Douwe Kiela, Kyunghyun Cho2021 · 412 citations
6. Do Prompt-Based Models Really Understand the Meaning of Their Prompts?Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Albert Webson, Ellie Pavlick2022 · 407 citations
7. 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
8. Generating Datasets with Pretrained Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Timo Schick, Hinrich Schutze2021 · 254 citations
9. Noisy Channel Language Model Prompting for Few-Shot Text ClassificationAnnual Meeting of the Association for Computational Linguistics (ACL)Sewon Min, Mike Lewis, Hannaneh Hajishirzi, et al.2021 · 243 citations
10. Reframing Instructional Prompts to GPTk's LanguageFindings of the Association for Computational LinguisticsSwaroop Mishra, Daniel Khashabi, Chitta Baral, et al.2022 · 242 citations
11. Generating Training Data with Language Models: Towards Zero-Shot Language UnderstandingarXiv:2202.04538Yu Meng, Jiaxin Huang, Yu Zhang, et al.2022 · 210 citations
12. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Archiki Prasad, Peter Hase, Xiang Zhou, et al.2023 · 184 citations
13. Label Verbalization and Entailment for Effective Zero and Few-Shot Relation ExtractionConference on Empirical Methods in Natural Language Processing (EMNLP)Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, et al.2021 · 167 citations
14. Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and EvaluationFindings of the Association for Computational LinguisticsMarius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.2023 · 148 citations
15. True Few-Shot Learning with Prompts–-A Real-World PerspectiveTransactions of the Association for Computational Linguistics (TACL)Timo Schick, Hinrich Schütze2022 · 96 citations
16. FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkarXiv:2107.07498Liang Xu, Xiaojing Lu, Chenyang Yuan, et al.2021 · 54 citations
17. Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and ChallengesAnnual Meeting of the Association for Computational Linguistics (ACL)Bolei Ma, Yuting Li, Wei Zhou, et al.2025 · 49 citations
18. ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot ScenariosTransactions of the Association for Computational Linguistics (TACL)Markus Bayer, Justin Lutz, Christian Reuter2026 · 16 citations
19. Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhuo Li, Yuhao Du, Jinpeng Hu, et al.2025 · 9 citations

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