---
title: "Who cites Text and Patterns: For Effective — 76 citing papers"
description: "76 papers of the Elamur Research library cite «Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango», found in their…"
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# Who cites «Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango»

76 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 «Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango»](https://md.research.elamur.ai/free/en/papers/madaan-2022-text-patterns-effective-chain-thought/)

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. [**ReAct: Synergizing Reasoning and Acting in Language Models arXiv:2210.03629**Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.*2022 · 2,591 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/)
3. **[Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,444 citations*
4. **[Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,119 citations*
5. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
6. **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 586 citations*
7. [**Large Language Models Can Be Easily Distracted by Irrelevant Context arXiv:2302.00093**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 534 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
8. [**Large Language Models as Optimizers arXiv:2309.03409**Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.*2023 · 413 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/)
9. [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
10. **[Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boshi Wang, Sewon Min, Xiang Deng, et al.*2023 · 272 citations*
11. [**Measuring Faithfulness in Chain-of-Thought Reasoning arXiv:2307.13702**Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/)
12. **[Language Models can Solve Computer Tasks](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 249 citations*
13. **[ToolQA: A Dataset for LLM Question Answering with External Tools](https://md.research.elamur.ai/free/en/papers/zhuang-2023-toolqa-dataset-llm-question-answering/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuchen Zhuang, Yue Yu, Kuan Wang, et al.*2023 · 182 citations*
14. [**Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution arXiv:2309.16797**Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/fernando-2023-promptbreeder-self-referential-self-improvement/)
15. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning arXiv:2409.12183**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 138 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
16. [**Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future arXiv:2309.15402**Zheng Chu, Jingchang Chen, Qianglong Chen, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-navigate-through-enigmatic-labyrinth-survey/)
17. **[The Impact of Reasoning Step Length on Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingyu Jin, Qinkai Yu, Dong Shu, et al.*2024 · 119 citations*
18. [**Teaching Algorithmic Reasoning via In-context Learning arXiv:2211.09066**Hattie Zhou, Azade Nova, Hugo Larochelle, et al.*2022 · 96 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/)
19. [**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/)
20. [**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/)
21. [**Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding arXiv:2401.12954**Mirac Suzgun, Adam Tauman Kalai*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2024-meta-prompting-enhancing-language-models/)
22. [**Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions arXiv:2307.13339**Skyler Wu, Eric Meng Shen, Charumathi Badrinath, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-analyzing-chain-thought-prompting-large/)
23. **[How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation](https://md.research.elamur.ai/free/en/papers/yang-2026-how-chain-thought-works-tracing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Yang, Qinghua Zhao, Lei Li, et al.*2026 · 1 citation*

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

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