🧑‍🔬 Who cites Yiben Yang

101 citations of their works from 101 papers of our library (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · ← Yiben Yang

1. 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
2. BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationarXiv:2201.12086Junnan Li, Dongxu Li, Caiming Xiong, et al.2022 · 1,523 citations
3. A Survey of Data Augmentation Approaches for NLParXiv:2105.03075Steven Y. Feng, Varun Gangal, Jason Wei, et al.2021 · 940 citations
4. oLMpics — On what Language Model Pre-training CapturesTransactions of the Association for Computational Linguistics (TACL)Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.2019 · 440 citations
5. True Few-Shot Learning with Language ModelsarXiv:2105.11447Ethan Perez, Douwe Kiela, Kyunghyun Cho2021 · 412 citations
6. 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
7. Do NLP Models Know Numbers? Probing Numeracy in EmbeddingsConference on Empirical Methods in Natural Language Processing (EMNLP)Eric Wallace, Yizhong Wang, Sujian Li, et al.2019 · 352 citations
8. Symbolic Knowledge Distillation: from General Language Models to Commonsense ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Peter West, Chandra Bhagavatula, Jack Hessel, et al.2022 · 349 citations
9. An Empirical Survey of Data Augmentation for Limited Data Learning in NLPTransactions of the Association for Computational Linguistics (TACL)Jiaao Chen, Derek Tam, Colin Raffel, et al.2023 · 254 citations
10. Generating Datasets with Pretrained Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Timo Schick, Hinrich Schutze2021 · 254 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. Generative Data Augmentation for Commonsense ReasoningFindings of the Association for Computational LinguisticsYiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.2020 · 198 citations
13. Explanations for CommonsenseQA: New Dataset and ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, et al.2021 · 164 citations
14. Leveraging Large Language Models for Multiple Choice Question AnsweringarXiv:2210.12353Joshua Robinson, Christopher Michael Rytting, David Wingate2022 · 157 citations
15. Revisiting DocRED - Addressing the False Negative Problem in Relation ExtractionConference on Empirical Methods in Natural Language Processing (EMNLP)Qingyu Tan, Lu Xu, Lidong Bing, et al.2022 · 151 citations
16. Is GPT-3 a Good Data Annotator?arXiv:2212.10450Bosheng Ding, Chengwei Qin, Linlin Liu, et al.2022 · 147 citations
17. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and ApproachesarXiv:1904.01172Shane Storks, Qiaozi Gao, Joyce Y. Chai2019 · 135 citations
18. Generating Data to Mitigate Spurious Correlations in Natural Language Inference DatasetsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuxiang Wu, Matt Gardner, Pontus Stenetorp, et al.2022 · 99 citations
19. True Few-Shot Learning with Prompts–-A Real-World PerspectiveTransactions of the Association for Computational Linguistics (TACL)Timo Schick, Hinrich Schütze2022 · 96 citations
20. Do Language Embeddings capture Scales?Findings of the Association for Computational LinguisticsXikun Zhang, Deepak Ramachandran, Ian Tenney, et al.2020 · 75 citations
21. InPars: Data Augmentation for Information Retrieval using Large Language ModelsarXiv:2202.05144Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, et al.2022 · 72 citations
22. Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and ChallengesarXiv:2309.12426Vinay Samuel, Houda Aynaou, Arijit Ghosh Chowdhury, et al.2023 · 16 citations
23. How Pre-trained Word Representations Capture Commonsense Physical ComparisonsWorkshop on Commonsense Inference in Natural Language ProcessingPranav Goel, Shi Feng, Jordan Boyd-Graber2019 · 12 citations
24. An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation RecognitionLanguage Resources and Evaluation Conference (LREC)Kazumasa Omura, Fei Cheng, Sadao Kurohashi2024 · 8 citations
25. Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMsLanguage Resources and Evaluation Conference (LREC)Kei Moriyama, Takashi Kodama, Kouta Nakayama2026

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