🔗 Who cites «LSTM Networks Can Perform Dynamic Counting»

41 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «LSTM Networks Can Perform Dynamic Counting»

1. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5BvosjAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,756 citations
2. Exploring Length Generalization in Large Language ModelsarXiv:2207.04901Cem Anil, Yuhuai Wu, Anders Andreassen, et al.2022 · 139 citations
3. Representing Numbers in NLP: a Survey and a VisionConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Avijit Thawani, Jay Pujara, Filip Ilievski, et al.2021 · 139 citations
4. On the Ability and Limitations of Transformers to Recognize Formal LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP)Satwik Bhattamishra, Kabir Ahuja, Navin Goyal2020 · 113 citations
5. Neural Networks and the Chomsky HierarchyarXiv:2207.02098Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, et al.2022 · 112 citations
6. RNNs can generate bounded hierarchical languages with optimal memoryConference on Empirical Methods in Natural Language Processing (EMNLP)John Hewitt, Michael Hahn, Surya Ganguli, et al.2020 · 65 citations
7. Targeted Syntactic Evaluation on the Chomsky HierarchyLanguage Resources and Evaluation Conference (LREC)Taiga Someya, Ryo Yoshida, Yohei Oseki2024 · 10 citations
8. Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic BiasesAnnual Meeting of the Association for Computational Linguistics (ACL)Michael Y. Hu, Jackson Petty, Chuan Shi, et al.2025 · 9 citations

33 more papers of our library cite it; they have no page here yet.

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