Emergent linguistic structure in artificial neural networks trained by self-supervision
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2020/06/03
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Indian UGC (journal)
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Refrences59
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Citations65
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Christopher D. Manning Computer Science Department, Stanford University, Stanford, CA 94305; ORCID (unauthenticated)
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Kevin Clark Computer Science Department, Stanford University, Stanford, CA 94305;
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John Hewitt Computer Science Department, Stanford University, Stanford, CA 94305; ORCID (unauthenticated)
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Urvashi Khandelwal Computer Science Department, Stanford University, Stanford, CA 94305;
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Omer Levy Facebook Artificial Intelligence Research, Facebook Inc., Seattle, WA 98109
Abstract
Cite
Manning, Christopher D., et al. “Emergent Linguistic Structure in Artificial Neural Networks Trained by Self-Supervision”. Proceedings of the National Academy of Sciences, vol. 117, no. 48, 2020, pp. 30046-54, https://doi.org/10.1073/pnas.1907367117.
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020). Emergent linguistic structure in artificial neural networks trained by self-supervision. Proceedings of the National Academy of Sciences, 117(48), 30046-30054. https://doi.org/10.1073/pnas.1907367117
Manning, Christopher D., Kevin Clark, John Hewitt, Urvashi Khandelwal, and Omer Levy. “Emergent Linguistic Structure in Artificial Neural Networks Trained by Self-Supervision”. Proceedings of the National Academy of Sciences 117, no. 48 (2020): 30046-54. https://doi.org/10.1073/pnas.1907367117.
Manning CD, Clark K, Hewitt J, Khandelwal U, Levy O. Emergent linguistic structure in artificial neural networks trained by self-supervision. Proceedings of the National Academy of Sciences. 2020;117(48):30046-54.
Journal Category
Science
Science (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| 10.1108/S0092-4563(2011)0000037009 | 2011 | |||
| Building a large annotated corpus of English: The Penn treebank | 1993 | |||
| Proceedings of the Second BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP | 2019 | |||
| Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | 2019 | |||
| Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | 2019 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines | Psychology & Marketing |
| 2024 | |
| The Life Cycle of Knowledge in Big Language Models: A Survey | Machine Intelligence Research |
| 1 | 2024 |
| Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval | Cell Reports |
| 2024 | |
| Generating meaning: active inference and the scope and limits of passive AI | Trends in Cognitive Sciences |
| 2 | 2024 |
| Dissociating language and thought in large language models | Trends in Cognitive Sciences |
| 7 | 2024 |
Citations Analysis
The category
Science: Science (General) 12
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Accuracy of Automated Sentiment Analysis and was published in 2019. The most recent citation comes from a 2024 study titled Explicitly Representing Syntax Improves Sentence-to-Layout Prediction of Unexpected Situations. This article reached its peak citation in 2022, with 20 citations. It has been cited in 48 different journals, 33% of which are open access. Among related journals, the Proceedings of the National Academy of Sciences cited this research the most, with 5 citations. The chart below illustrates the annual citation trends for this article.