--- title: "Who cites A Latent Variable Model — 81 citing papers" description: "81 papers of the Elamur Research library cite «A Latent Variable Model Approach to PMI-based Word Embeddings», found in their…" canonical: "https://research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Latent Variable Model Approach to PMI-based…](https://md.research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/) / cited by # Who cites «A Latent Variable Model Approach to PMI-based Word Embeddings» 81 papers of our library cite it (of the 141,840 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «A Latent Variable Model Approach to PMI-based Word Embeddings»](https://md.research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/) 1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,056 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) **[Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features](https://md.research.elamur.ai/free/en/papers/pagliardini-2018-unsupervised-learning-sentence-embeddings-compositional/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Matteo Pagliardini, Prakhar Gupta, Martin Jaggi*2018 · 842 citations* **[Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://md.research.elamur.ai/free/en/papers/arora-2018-linear-algebraic-structure-word-senses/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sanjeev Arora, Yuanzhi Li, Yingyu Liang, et al.*2018 · 248 citations* **[Understanding Undesirable Word Embedding Associations](https://md.research.elamur.ai/free/en/papers/ethayarajh-2019-understanding-undesirable-word-embedding-associations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kawin Ethayarajh, David Duvenaud, Graeme Hirst*2019 · 171 citations* [**LESS: Selecting Influential Data for Targeted Instruction Tuning arXiv:2402.04333**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/) **[The Linear Representation Hypothesis and the Geometry of Large Language Models](https://md.research.elamur.ai/free/en/papers/park-2023-linear-representation-hypothesis-geometry-large/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Kiho Park, Yo Joong Choe, Victor Veitch*2023 · 157 citations* **[Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders](https://md.research.elamur.ai/free/en/papers/liu-2021-fast-effective-self-supervised-transforming/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fangyu Liu, Ivan Vulić, Anna Korhonen, et al.*2021 · 147 citations* **[Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures](https://md.research.elamur.ai/free/en/papers/choubey-2018-improving-event-coreference-resolution-modeling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Prafulla Kumar Choubey, Ruihong Huang*2018 · 43 citations* [**Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic arXiv:2205.10412 · In Proceedings of the 11th Workshop on Semantic Evaluation (SemEval2021), 2021 International Conference on Information Technology (ICIT), 2021, pp. 428-434, IEEE**Eman Naser-Karajah, Nabil Arman, Mustafa Jarrar*2021 · 14 citations*](https://md.research.elamur.ai/free/en/papers/naser-karajah-2022-current-trends-approaches-synonyms-extraction/) [**Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic**Ido Benbaji, Omri Doron, Adele Henot-Mortier*2022 · 1 citation*](https://md.research.elamur.ai/free/en/papers/benbaji-2022-word-embeddings-distinguish-denominal-root/) **[“Oat Milk Vegan Chocolate Taste Great!”: Monitoring the Food Transition Debate in Reddit](https://md.research.elamur.ai/free/en/papers/zella-2026-oat-milk-vegan-chocolate-taste/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Greta Zella, Jan Willem Bolderdijk, Saskia Peels, et al.*2026* 70 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/) — A Latent Variable Model Approach to PMI-based Word Embeddings