--- title: "Who cites The Impact of Positional Encoding — 91 citing papers" description: "91 papers of the Elamur Research library cite «The Impact of Positional Encoding on Length Generalization in Transformers», found in their…" canonical: "https://research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The Impact of Positional Encoding on Length…](https://md.research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/) / cited by # Who cites «The Impact of Positional Encoding on Length Generalization in Transformers» 91 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «The Impact of Positional Encoding on Length Generalization in Transformers»](https://md.research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/) 1. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations* 2. [**Code Llama: Open Foundation Models for Code arXiv:2308.12950**Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.*2024 · 1,430 citations*](https://md.research.elamur.ai/free/en/papers/roziere-2024-code-llama-open-foundation-models/) 3. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) 4. **[LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models](https://md.research.elamur.ai/free/en/papers/han-2024-lm-infinite-zero-shot-extreme/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chi Han, Qifan Wang, Hao Peng, et al.*2024 · 95 citations* 5. [**Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention arXiv:2404.07143**Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/munkhdalai-2024-leave-no-context-behind-efficient/) 6. **[Mission: Impossible Language Models](https://md.research.elamur.ai/free/en/papers/kallini-2024-mission-impossible-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Julie Kallini, Isabel Papadimitriou, Richard Futrell, et al.*2024 · 70 citations* 7. [**What Algorithms can Transformers Learn? A Study in Length Generalization arXiv:2310.16028**Hattie Zhou, Arwen Bradley, Etai Littwin, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/) 8. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/) 9. [**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/) 10. **[Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum](https://md.research.elamur.ai/free/en/papers/pouransari-2024-dataset-decomposition-faster-llm-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, et al.*2024 · 16 citations* 11. [**Base of RoPE Bounds Context Length arXiv:2405.14591**Xin Men, Mingyu Xu, Bingning Wang, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/men-2024-base-rope-bounds-context/) 12. **[Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension](https://md.research.elamur.ai/free/en/papers/zhao-2026-adaptive-zooming-relevance-informed-positional/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongbo Zhao, Huibin Wang, Bin Tang, et al.*2026* 13. **[Position Encoding with Random Float Sampling Enhances Length Generalization of Transformers](https://md.research.elamur.ai/free/en/papers/shimizu-2026-position-encoding-random-float-sampling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Atsushi Shimizu, Shohei Taniguchi, Yutaka Matsuo*2026* 14. **[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* 77 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/) — The Impact of Positional Encoding on Length Generalization in Transformers