--- title: "Who cites Rakesh Menon — 80 papers of our library" description: "80 papers of the Elamur Research library cite the works of Rakesh Menon, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/rakesh-menon/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/rakesh-menon/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Rakesh Menon](https://md.research.elamur.ai/free/en/authors/rakesh-menon/) / cited by # Who cites Rakesh Menon 80 citations of their works from 80 papers of our library (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← Rakesh Menon](https://md.research.elamur.ai/free/en/authors/rakesh-menon/) 1. **[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* 2. [**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/) 3. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations* 4. [**Atlas: Few-shot Learning with Retrieval Augmented Language Models arXiv:2208.03299**Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.*2022 · 673 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/) 5. [**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/) 6. **[Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Albert Webson, Ellie Pavlick*2022 · 407 citations* 7. **[RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 338 citations* 8. **[Generating Datasets with Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schutze*2021 · 254 citations* 9. **[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* 10. **[Reframing Instructional Prompts to GPTk's Language](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al.*2022 · 242 citations* 11. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/) 12. **[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* 13. **[GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 184 citations* 14. **[Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction](https://md.research.elamur.ai/free/en/papers/sainz-2021-label-verbalization-entailment-effective-zero/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, et al.*2021 · 167 citations* 15. **[Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.*2023 · 148 citations* 16. **[True Few-Shot Learning with Prompts–-A Real-World Perspective](https://md.research.elamur.ai/free/en/papers/schick-2022-true-few-shot-learning-prompts/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Timo Schick, Hinrich Schütze*2022 · 96 citations* 17. [**FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark arXiv:2107.07498**Liang Xu, Xiaojing Lu, Chenyang Yuan, et al.*2021 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-fewclue-chinese-few-shot/) 18. **[Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/ma-2025-pragmatics-era-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bolei Ma, Yuting Li, Wei Zhou, et al.*2025 · 49 citations* 19. **[ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios](https://md.research.elamur.ai/free/en/papers/bayer-2026-activellm-large-language-model-based/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Markus Bayer, Justin Lutz, Christian Reuter*2026 · 16 citations* 20. **[Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs](https://md.research.elamur.ai/free/en/papers/li-2025-self-instructed-derived-prompt-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuo Li, Yuhao Du, Jinpeng Hu, et al.*2025 · 9 citations* 60 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/rakesh-menon/) — Rakesh Menon