--- title: "Who cites Length Generalization in Arithmetic Transformers" description: "27 papers of the Elamur Research library cite «Length Generalization in Arithmetic Transformers», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Length Generalization in Arithmetic Transformers](https://md.research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/) / cited by # Who cites «Length Generalization in Arithmetic Transformers» 27 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «Length Generalization in Arithmetic Transformers»](https://md.research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/) 1. [**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/) 2. [**Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process arXiv:2407.20311**Tian Ye, Zicheng Xu, Yuanzhi Li, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-physics-language-models-part-2/) 3. [**Repeat After Me: Transformers are Better than State Space Models at Copying arXiv:2402.01032**Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/) 4. [**GPT Can Solve Mathematical Problems Without a Calculator arXiv:2309.03241**Zhen Yang, Ming Ding, Qingsong Lv, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-gpt-solve-mathematical-problems-calculator/) 5. **[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* 6. [**Positional Description Matters for Transformers Arithmetic arXiv:2311.14737**Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-positional-description-matters-transformers-arithmetic/) 7. [**Transformers Can Achieve Length Generalization But Not Robustly arXiv:2402.09371**Yongchao Zhou, Uri Alon, Xinyun Chen, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-transformers-achieve-length-generalization-but/) 8. **[Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers](https://md.research.elamur.ai/free/en/papers/alfarano-2024-global-lyapunov-functions-long-standing/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alberto Alfarano, François Charton, Amaury Hayat*2024 · 19 citations* 9. [**Chain-of-Thought Reasoning is a Policy Improvement Operator arXiv:2309.08589**Hugh Zhang, David C. Parkes*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-chain-thought-reasoning-policy-improvement/) 10. [**SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training arXiv:2310.02227**Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/meidani-2023-snip-bridging-mathematical-symbolic-numeric/) 11. [**Improving Length-Generalization in Transformers via Task Hinting arXiv:2310.00726**Pranjal Awasthi, Anupam Gupta*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/awasthi-2023-improving-length-generalization-transformers-task/) 12. [**Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks arXiv:2406.01895**Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/sabbaghi-2024-explicitly-encoding-structural-symmetry-key/) 15 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/) — Length Generalization in Arithmetic Transformers