--- title: "Who cites Learning and Evaluating General Linguistic Intelligence" description: "104 papers of the Elamur Research library cite «Learning and Evaluating General Linguistic Intelligence», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Learning and Evaluating General Linguistic…](https://md.research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/) / cited by # Who cites «Learning and Evaluating General Linguistic Intelligence» 104 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 «Learning and Evaluating General Linguistic Intelligence»](https://md.research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/) 1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/) 2. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/) 3. [**Unified Language Model Pre-training for Natural Language Understanding and Generation arXiv:1905.03197**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,422 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/) 4. [**Calibrate Before Use: Improving Few-Shot Performance of Language Models arXiv:2102.09690**Tony Z. Zhao, Eric Wallace, Shi Feng, et al.*2021 · 825 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/) 5. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model arXiv:2201.11990**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 615 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/) 6. **[Pretrained Transformers Improve Out-of-Distribution Robustness](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 452 citations* 7. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 440 citations* 8. **[Information-Theoretic Probing with Minimum Description Length](https://md.research.elamur.ai/free/en/papers/voita-2020-information-theoretic-probing-minimum-description/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Elena Voita, Ivan Titov*2020 · 345 citations* 9. **[Can You Unpack That? Learning to Rewrite Questions-in-Context](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber*2019 · 245 citations* 10. **[Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling](https://md.research.elamur.ai/free/en/papers/han-2019-unsupervised-domain-adaptation-contextualized-embeddings/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaochuang Han, Jacob Eisenstein*2019 · 241 citations* 11. **[MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://md.research.elamur.ai/free/en/papers/talmor-2019-multiqa-empirical-investigation-generalization-transfer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Talmor, Jonathan Berant*2019 · 236 citations* 12. **[Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 220 citations* 13. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 220 citations* 14. **[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* 15. **[What to Pre-Train on? Efficient Intermediate Task Selection](https://md.research.elamur.ai/free/en/papers/poth-2021-what-pre-train-efficient-intermediate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.*2021 · 132 citations* 16. **[English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://md.research.elamur.ai/free/en/papers/phang-2020-english-intermediate-task-training-improves/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Jason Phang, Iacer Calixto, Phu Mon Htut, et al.*2020 · 70 citations* 17. **[Investigating Language Relationships in Multilingual Sentence Encoders Through the Lens of Linguistic Typology](https://md.research.elamur.ai/free/en/papers/choenni-2022-investigating-language-relationships-multilingual-sentence/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Rochelle Choenni, Ekaterina Shutova*2022 · 24 citations* 18. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/) 19. **[Sparse Adapter Fusion for Continual Learning in NLP](https://md.research.elamur.ai/free/en/papers/zeng-2026-sparse-adapter-fusion-continual-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Min Zeng, Xi Chen, Haiqin Yang, et al.*2026 · 1 citation* 85 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/) — Learning and Evaluating General Linguistic Intelligence