---
title: "Who cites Self-Attention Networks Can — 42 citing papers"
description: "42 papers of the Elamur Research library cite «Self-Attention Networks Can Process Bounded Hierarchical Languages», found in their…"
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# 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»](https://md.research.elamur.ai/free/en/papers/yao-2021-self-attention-networks-process-bounded/)

1. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
2. **[Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity](https://md.research.elamur.ai/free/en/papers/hao-2022-formal-language-recognition-hard-attention/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yiding Hao, Dana Angluin, Robert Frank*2022 · 62 citations*
3. [**Language Models as Models of Language arXiv:2408.07144**Raphael Milliere*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/milliere-2024-language-models-models-language/)
4. [**RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval arXiv:2402.18510**Kaiyue Wen, Xingyu Dang, Kaifeng Lyu*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/wen-2024-rnns-transformers-yet-key-bottleneck/)
5. [**Can Transformers Learn to Solve Problems Recursively? arXiv:2305.14699**Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-transformers-learn-solve-problems-recursively/)
6. **[Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth](https://md.research.elamur.ai/free/en/papers/yuan-2026-barriers-discrete-reasoning-transformers-survey/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/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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