--- title: "Who cites To Tune or Not to Tune? — 109 citing papers" description: "109 papers of the Elamur Research library cite «To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks», found in their…" canonical: "https://research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [To Tune or Not to Tune? Adapting Pretrained…](https://md.research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/) / cited by # Who cites «To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks» 109 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks»](https://md.research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/) 1. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/) 2. **[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* 3. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/) 4. **[Linguistic Knowledge and Transferability of Contextual Representations](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,021 citations* 5. **[Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 564 citations* 6. [**Revisiting Few-sample BERT Fine-tuning arXiv:2006.05987**Tianyi Zhang, Felix Wu, Arzoo Katiyar, et al.*2020 · 368 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-revisiting-few-sample-bert-fine/) 7. **[SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 304 citations* 8. **[Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, et al.*2021 · 258 citations* 9. **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 222 citations* 10. [**Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing**Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2021-trankit-light-weight-transformer-based/) 11. **[Analyzing Dataset Annotation Quality Management in the Wild](https://md.research.elamur.ai/free/en/papers/klie-2024-analyzing-dataset-annotation-quality-management/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych*2024 · 76 citations* 12. [**Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future**Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/klie-2023-annotation-error-detection-analyzing-past/) 13. **[Cross-lingual Zero Pronoun Resolution](https://md.research.elamur.ai/free/en/papers/aloraini-2020-cross-lingual-zero-pronoun-resolution/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Abdulrahman Aloraini, Massimo Poesio*2020 · 20 citations* 14. **[Anaphoric Zero Pronoun Identification: A Multilingual Approach](https://md.research.elamur.ai/free/en/papers/aloraini-2020-anaphoric-zero-pronoun-identification-multilingual/) [Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC)](https://md.research.elamur.ai/free/en/venues/crac/)**Abdulrahman Aloraini, Massimo Poesio*2020 · 10 citations* 15. [**Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuning**Bill Noble, Vladislav Maraev*2021 · 7 citations*](https://md.research.elamur.ai/free/en/papers/noble-2021-large-scale-text-pre-training/) 16. **[ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction?](https://md.research.elamur.ai/free/en/papers/zangari-2025-me2-bert-are-events-emotions/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Lorenzo Zangari, Candida M. Greco, Davide Picca, et al.*2025 · 5 citations* 17. **[Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training](https://md.research.elamur.ai/free/en/papers/gupta-2021-multilingual-multilabel-emotion-recognition-virtual/) [Workshop on Multilingual Representation Learning](https://md.research.elamur.ai/free/en/venues/mrl/)**Vikram Gupta*2021 · 5 citations* 18. **[EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms](https://md.research.elamur.ai/free/en/papers/nguyen-2026-edupulse-practical-llm-enhanced-opinion/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Xuan Phuc Nguyen, Xuan Phi Nguyen, Vinh Tiep Nguyen, et al.*2026* 19. **[Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL](https://md.research.elamur.ai/free/en/papers/wu-2026-parallel-sft-improving-zero-shot/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhaofeng Wu, Shiqi Wang, Boya Peng, et al.*2026* 90 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/) — To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks