--- title: "Who cites What Algorithms can — 59 citing papers" description: "59 papers of the Elamur Research library cite «What Algorithms can Transformers Learn? A Study in Length Generalization», found in their…" canonical: "https://research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [What Algorithms can Transformers Learn? A Study…](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/) / cited by # Who cites «What Algorithms can Transformers Learn? A Study in Length Generalization» 59 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «What Algorithms can Transformers Learn? A Study in Length Generalization»](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/) 1. [**GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models arXiv:2410.05229**Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/) 2. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution arXiv:2401.03065**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/) 3. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/) 4. [**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/) 5. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/) 6. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/) 7. [**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/) 8. [**Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs arXiv:2402.14903**Aaditya K. Singh, DJ Strouse*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-tokenization-counts-impact-tokenization-arithmetic/) 9. **[Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases](https://md.research.elamur.ai/free/en/papers/hu-2025-between-circuits-chomsky-pre-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michael Y. Hu, Jackson Petty, Chuan Shi, et al.*2025 · 9 citations* 10. **[GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-gsm-noise-exploring-enhancing-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengxin Zhang, Chengyu Huang, Xufu Liu, et al.*2026* 49 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/) — What Algorithms can Transformers Learn? A Study in Length Generalization