---
title: "Who cites Transformers Can Achieve — 25 citing papers"
description: "25 papers of the Elamur Research library cite «Transformers Can Achieve Length Generalization But Not Robustly», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2024-transformers-achieve-length-generalization-but/cited-by/"
---
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# Who cites «Transformers Can Achieve Length Generalization But Not Robustly»

25 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «Transformers Can Achieve Length Generalization But Not Robustly»](https://md.research.elamur.ai/free/en/papers/zhou-2024-transformers-achieve-length-generalization-but/)

1. [**Premise Order Matters in Reasoning with Large Language Models arXiv:2402.08939**Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-premise-order-matters-reasoning-large/)
2. **[Transformers Can Do Arithmetic with the Right Embeddings](https://md.research.elamur.ai/free/en/papers/mcleish-2024-transformers-arithmetic-right-embeddings/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sean McLeish, Arpit Bansal, Alex Stein, et al.*2024 · 36 citations*
3. **[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*
4. [**Looped Transformers for Length Generalization arXiv:2409.15647**Ying Fan, Yilun Du, Kannan Ramchandran, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/fan-2024-looped-transformers-length-generalization/)
5. **[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*

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

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