--- title: "Who cites E-BERT: Efficient-Yet-Effective — 104 citing papers" description: "104 papers of the Elamur Research library cite «E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/poerner-2019-e-bert-efficient-yet-effective/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/poerner-2019-e-bert-efficient-yet-effective/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [E-BERT: Efficient-Yet-Effective Entity Embeddings…](https://md.research.elamur.ai/free/en/papers/poerner-2019-e-bert-efficient-yet-effective/) / cited by # Who cites «E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT» 104 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← back to «E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT»](https://md.research.elamur.ai/free/en/papers/poerner-2019-e-bert-efficient-yet-effective/) 1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 2. [**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/) 3. **[How Can We Know What Language Models Know?](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,521 citations* 4. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters arXiv:2002.01808**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 666 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/) 5. **[Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://md.research.elamur.ai/free/en/papers/kassner-2020-negated-misprimed-probes-pretrained-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nora Kassner, Hinrich Schütze*2020 · 435 citations* 6. **[Factual Probing Is MASK: Learning vs. Learning to Recall](https://md.research.elamur.ai/free/en/papers/zhong-2021-factual-probing-is-mask-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zexuan Zhong, Dan Friedman, Danqi Chen*2021 · 415 citations* 7. [**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/) 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. **[CoLAKE: Contextualized Language and Knowledge Embedding](https://md.research.elamur.ai/free/en/papers/sun-2020-colake-contextualized-language-knowledge-embedding/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Tianxiang Sun, Yunfan Shao, Xipeng Qiu, et al.*2020 · 239 citations* 10. [**How Context Affects Language Models' Factual Predictions arXiv:2005.04611**Fabio Petroni, Patrick Lewis, Aleksandra Piktus, et al.*2020 · 234 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-context-affects-language-models-factual/) 11. **[Entities as Experts: Sparse Memory Access with Entity Supervision](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 208 citations* 12. **[Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/kassner-2021-multilingual-lama-investigating-knowledge-multilingual/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Nora Kassner, Philipp Dufter, Hinrich Schütze*2021 · 180 citations* 13. [**A Review on Language Models as Knowledge Bases arXiv:2204.06031**Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-review-language-models-knowledge-bases/) 14. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations* 15. **[Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases](https://md.research.elamur.ai/free/en/papers/cao-2021-knowledgeable-or-educated-guess-revisiting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2021 · 141 citations* 16. **[Can Language Models be Biomedical Knowledge Bases?](https://md.research.elamur.ai/free/en/papers/sung-2021-can-language-models-be-biomedical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mujeen Sung, Jinhyuk Lee, Sean Yi, et al.*2021 · 127 citations* 17. **[Calibrating Factual Knowledge in Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/dong-2022-calibrating-factual-knowledge-pretrained-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingxiu Dong, Damai Dai, Yifan Song, et al.*2022 · 115 citations* 18. **[Understanding Transformer Memorization Recall Through Idioms](https://md.research.elamur.ai/free/en/papers/haviv-2023-understanding-transformer-memorization-recall-through/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adi Haviv, Ido Cohen, Jacob Gidron, et al.*2023 · 62 citations* 19. **[Combining BERT with Static Word Embeddings for Categorizing Social Media](https://md.research.elamur.ai/free/en/papers/alghanmi-2020-combining-bert-static-word-embeddings/) [Workshop on Noisy User-generated Text](https://md.research.elamur.ai/free/en/venues/wnut/)**Israa Alghanmi, Luis Espinosa Anke, Steven Schockaert*2020 · 40 citations* 20. [**The Life Cycle of Knowledge in Big Language Models: A Survey arXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2023 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-life-cycle-knowledge-big-language/) 21. **[Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification](https://md.research.elamur.ai/free/en/papers/hengle-2021-combining-context-free-contextualized-representations/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Amey Hengle, Atharva Kshirsagar, Shaily Desai, et al.*2021 · 6 citations* 22. **[Survey on Commonsense Question Answering (常识问答研究综述)](https://md.research.elamur.ai/free/en/papers/fan-2023-commonsense-question-answering/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yifan Fan (范怡帆), Bowei Zou (邹博伟), Qingting Xu (徐庆婷), et al.*2023* 82 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/poerner-2019-e-bert-efficient-yet-effective/) — E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT