🔗 Who cites «Self-Attention Networks Can Process Bounded Hierarchical Languages»

42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · ← back to «Self-Attention Networks Can Process Bounded Hierarchical Languages»

1. What Can Transformers Learn In-Context? A Case Study of Simple Function ClassesConference on Neural Information Processing Systems (NeurIPS)Shivam Garg, Dimitris Tsipras, Percy Liang, et al.2022 · 209 citations
2. Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit ComplexityTransactions of the Association for Computational Linguistics (TACL)Yiding Hao, Dana Angluin, Robert Frank2022 · 62 citations
3. Language Models as Models of LanguagearXiv:2408.07144Raphael Milliere2024 · 21 citations
4. RNNs are not Transformers (Yet): The Key Bottleneck on In-context RetrievalarXiv:2402.18510Kaiyue Wen, Xingyu Dang, Kaifeng Lyu2024 · 17 citations
5. Can Transformers Learn to Solve Problems Recursively?arXiv:2305.14699Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, et al.2023 · 17 citations
6. Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and BandwidthConference of the European Chapter of the Association for Computational Linguistics (EACL)Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, et al.2026 · 3 citations

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

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