🔗 Who cites «The Turking Test: Can Language Models Understand Instructions?»

60 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · ← back to «The Turking Test: Can Language Models Understand Instructions?»

1. Finetuned Language Models Are Zero-Shot LearnersarXiv:2109.01652Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.2021 · 1,954 citations
2. 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
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. Unnatural Instructions: Tuning Language Models with (Almost) No Human LaborAnnual Meeting of the Association for Computational Linguistics (ACL)Or Honovich, Thomas Scialom, Omer Levy, et al.2023 · 366 citations
5. 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
6. Discovering Language Model Behaviors with Model-Written EvaluationsarXiv:2212.09251Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.2022 · 328 citations
7. Large Language Models for Data Annotation and Synthesis: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Tan, Dawei Li, Song Wang, et al.2024 · 262 citations
8. Generating Datasets with Pretrained Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Timo Schick, Hinrich Schutze2021 · 254 citations
9. Reframing Instructional Prompts to GPTk's LanguageFindings of the Association for Computational LinguisticsSwaroop Mishra, Daniel Khashabi, Chitta Baral, et al.2022 · 242 citations
10. 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
11. Instruction Induction: From Few Examples to Natural Language Task DescriptionsAnnual Meeting of the Association for Computational Linguistics (ACL)Or Honovich, Uri Shaham, Samuel R. Bowman, et al.2022 · 148 citations
12. ZeroSCROLLS: A Zero-Shot Benchmark for Long Text UnderstandingFindings of the Association for Computational LinguisticsUri Shaham, Maor Ivgi, Avia Efrat, et al.2023 · 132 citations
13. Evaluating the Moral Beliefs Encoded in LLMsConference on Neural Information Processing Systems (NeurIPS)Nino Scherrer, Claudia Shi, Amir Feder, et al.2023 · 129 citations
14. True Few-Shot Learning with Prompts–-A Real-World PerspectiveTransactions of the Association for Computational Linguistics (TACL)Timo Schick, Hinrich Schütze2022 · 96 citations
15. 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
16. The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMsConference on Neural Information Processing Systems (NeurIPS)Laura Ruis, Akbir Khan, Stella Biderman, et al.2022 · 86 citations
17. Large Language Model Instruction Following: A Survey of Progresses and ChallengesComputational LinguisticsRenze Lou, Kai Zhang, Wenpeng Yin2024 · 83 citations
18. Finding Support Examples for In-Context LearningFindings of the Association for Computational LinguisticsXiaonan Li, Xipeng Qiu2023 · 78 citations
19. In-context Pretraining: Language Modeling Beyond Document BoundariesarXiv:2310.10638Weijia Shi, Sewon Min, Maria Lomeli, et al.2023 · 61 citations
20. The Validity of Coreference-based Evaluations of Natural Language UnderstandingarXiv:2602.16200Ian Porada2026

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