🔗 Who cites «Teaching Arithmetic to Small Transformers»

50 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · ← back to «Teaching Arithmetic to Small Transformers»

1. Unleashing the potential of prompt engineering for large language modelsarXiv:2310.14735 · Patterns 6(6) 101,260 (2025)Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.2023 · 536 citations
2. CRUXEval: A Benchmark for Code Reasoning, Understanding and ExecutionarXiv:2401.03065Alex Gu, Baptiste Rozière, Hugh Leather, et al.2024 · 122 citations
3. What Algorithms can Transformers Learn? A Study in Length GeneralizationarXiv:2310.16028Hattie Zhou, Arwen Bradley, Etai Littwin, et al.2023 · 67 citations
4. Tokenization counts: the impact of tokenization on arithmetic in frontier LLMsarXiv:2402.14903Aaditya K. Singh, DJ Strouse2024 · 55 citations
5. Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and BandwidthConference of the European Chapter of the Association for Computational Linguistics (EACL)Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, et al.2026 · 3 citations
6. From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMsFindings of the Association for Computational LinguisticsShaojie Wang, Liang Zhang2026

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

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