--- title: "Who cites Are Transformers universal — 61 citing papers" description: "61 papers of the Elamur Research library cite «Are Transformers universal approximators of sequence-to-sequence functions?», found in their…" canonical: "https://research.elamur.ai/free/en/papers/yun-2019-transformers-universal-approximators-sequence-sequence/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yun-2019-transformers-universal-approximators-sequence-sequence/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Are Transformers universal approximators of…](https://md.research.elamur.ai/free/en/papers/yun-2019-transformers-universal-approximators-sequence-sequence/) / cited by # Who cites «Are Transformers universal approximators of sequence-to-sequence functions?» 61 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Are Transformers universal approximators of sequence-to-sequence functions?»](https://md.research.elamur.ai/free/en/papers/yun-2019-transformers-universal-approximators-sequence-sequence/) 1. [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 3,028 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/) 2. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,023 citations* 3. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/) 4. **[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* 5. **[On the Ability and Limitations of Transformers to Recognize Formal Languages](https://md.research.elamur.ai/free/en/papers/bhattamishra-2020-ability-limitations-transformers-recognize-formal/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Satwik Bhattamishra, Kabir Ahuja, Navin Goyal*2020 · 113 citations* 6. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/) 7. **[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* 8. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/) 9. **[Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling](https://md.research.elamur.ai/free/en/papers/rodkin-2026-beyond-memorization-extending-reasoning-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ivan Rodkin, Daniil Orel, Konstantin Smirnov, et al.*2026* 10. **[Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting Circuits](https://md.research.elamur.ai/free/en/papers/zhao-2026-do-transformers-grok-succinct-algorithms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizheng Zhao*2026* 11. **[Language Acquisition Device in Large Language Models](https://md.research.elamur.ai/free/en/papers/mita-2026-language-acquisition-device-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Masato Mita, Taiga Someya, Ryo Yoshida, et al.*2026* 12. **[SCOPE: Boosting LLM Efficiency with Scoped Position Encoding](https://md.research.elamur.ai/free/en/papers/qi-2026-scope-boosting-llm-efficiency-scoped/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingguo Qi, Hongyang Chen, Zhao Li*2026* 13. **[Understanding the Prompt Sensitivity](https://md.research.elamur.ai/free/en/papers/liu-2026-understanding-prompt-sensitivity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Liu, Chenhui Chu*2026* 48 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/yun-2019-transformers-universal-approximators-sequence-sequence/) — Are Transformers universal approximators of sequence-to-sequence functions?