--- title: "Who cites Teaching Algorithmic Reasoning — 73 citing papers" description: "73 papers of the Elamur Research library cite «Teaching Algorithmic Reasoning via In-context Learning», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Teaching Algorithmic Reasoning via In-context…](https://md.research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/) / cited by # Who cites «Teaching Algorithmic Reasoning via In-context Learning» 73 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · [← back to «Teaching Algorithmic Reasoning via In-context Learning»](https://md.research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/) 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. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/) 3. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 780 citations* 4. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/) 5. **[Faith and Fate: Limits of Transformers on Compositionality](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 327 citations* 6. [**Llemma: An Open Language Model For Mathematics arXiv:2310.10631**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/) 7. [**Reasoning with Language Model Prompting: A Survey arXiv:2212.09597**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/) 8. **[Deductive Verification of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 130 citations* 9. **[Can Language Models Solve Graph Problems in Natural Language?](https://md.research.elamur.ai/free/en/papers/wang-2023-language-models-solve-graph-problems/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Heng Wang, Shangbin Feng, Tianxing He, et al.*2023 · 124 citations* 10. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/) 11. **[Complementary Explanations for Effective In-Context Learning](https://md.research.elamur.ai/free/en/papers/ye-2023-complementary-explanations-effective-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.*2023 · 106 citations* 12. **[The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://md.research.elamur.ai/free/en/papers/shojaee-2025-illusion-thinking-understanding-strengths-limitations/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, et al.*2025 · 104 citations* 13. **[LAMBADA: Backward Chaining for Automated Reasoning in Natural Language](https://md.research.elamur.ai/free/en/papers/kazemi-2023-lambada-backward-chaining-automated-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Najoung Kim, Deepti Bhatia, et al.*2023 · 89 citations* 14. [**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/) 15. [**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/) 16. [**Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks arXiv:2305.14201**Tiedong Liu, Bryan Kian Hsiang Low*2023 · 65 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-goat-fine-tuned-llama-outperforms/) 17. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/) 18. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations* 19. [**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/) 20. **[Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus](https://md.research.elamur.ai/free/en/papers/lee-2024-reasoning-abilities-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Seungpil Lee, Woochang Sim, Donghyeon Shin, et al.*2024 · 38 citations* 21. [**Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models arXiv:2308.00304**Jiaao Chen, Xiaoman Pan, Dian Yu, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-skills-context-prompting-unlocking-compositionality/) 22. **[CAST: Achieving Stable LLM-based Text Analysis for Data Analytics](https://md.research.elamur.ai/free/en/papers/xie-2026-cast-achieving-stable-llm-based/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinxiang Xie, Zihao Li, Wei He, et al.*2026* 23. **[Evidence-Augmented Generation Reasoning for Extremely Low-Resource Language Decipherment](https://md.research.elamur.ai/free/en/papers/zhu-2026-evidence-augmented-generation-reasoning-extremely/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Xiaoyu Zhu, Long Yuan, Rui Qi, et al.*2026* 50 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/) — Teaching Algorithmic Reasoning via In-context Learning