--- title: "Who cites Repeat After Me: Transformers — 54 citing papers" description: "54 papers of the Elamur Research library cite «Repeat After Me: Transformers are Better than State Space Models at Copying», found in their…" canonical: "https://research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Repeat After Me: Transformers are Better than…](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/) / cited by # Who cites «Repeat After Me: Transformers are Better than State Space Models at Copying» 54 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 «Repeat After Me: Transformers are Better than State Space Models at Copying»](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/) 1. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/) 2. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 89 citations* 3. [**An Empirical Study of Mamba-based Language Models arXiv:2406.07887**Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.*2024 · 67 citations*](https://md.research.elamur.ai/free/en/papers/waleffe-2024-empirical-study-mamba-based-language/) 4. [**LAB-Bench: Measuring Capabilities of Language Models for Biology Research arXiv:2407.10362**Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/laurent-2024-lab-bench-measuring-capabilities/) 5. [**Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling arXiv:2406.07522**Liliang Ren, Yang Liu, Yadong Lu, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/ren-2024-samba-simple-hybrid-state-space/) 6. [**Kimi Linear: An Expressive, Efficient Attention Architecture arXiv:2510.26692**Kimi Team, Yu Zhang, Zongyu Lin, et al.*2025 · 51 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-linear-expressive-efficient/) 7. [**Hymba: A Hybrid-head Architecture for Small Language Models arXiv:2411.13676**Xin Dong, Yonggan Fu, Shizhe Diao, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-hymba-hybrid-head-architecture-small/) 8. [**Zamba: A Compact 7B SSM Hybrid Model arXiv:2405.16712**Paolo Glorioso, Quentin Anthony, Yury Tokpanov, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/glorioso-2024-zamba-compact-7b-ssm-hybrid/) 9. **[Long-Context Language Modeling with Parallel Context Encoding](https://md.research.elamur.ai/free/en/papers/yen-2024-long-context-language-modeling-parallel/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Howard Yen, Tianyu Gao, Danqi Chen*2024 · 44 citations* 10. [**Physics of Language Models: Part 1, Learning Hierarchical Language Structures arXiv:2305.13673**Zeyuan Allen-Zhu, Yuanzhi Li*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/allen-zhu-2023-physics-language-models-part-1/) 11. [**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/) 12. [**Associative Recurrent Memory Transformer arXiv:2407.04841**Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/rodkin-2024-associative-recurrent-memory-transformer/) 13. **[StateX: Enhancing RNN Recall via Post-training State Expansion](https://md.research.elamur.ai/free/en/papers/shen-2026-statex-enhancing-rnn-recall-post/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingyu Shen, Yingfa Chen, Zhen Leng Thai, et al.*2026 · 1 citation* 41 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/) — Repeat After Me: Transformers are Better than State Space Models at Copying