🔗 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»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. ReAct: Synergizing Reasoning and Acting in Language ModelsarXiv:2210.03629Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.2022 · 2,591 citations
3. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?Conference on Empirical Methods in Natural Language Processing (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 ThemFindings of the Association for Computational LinguisticsMirac Suzgun, Nathan Scales, Nathanael Schärli, et al.2023 · 1,119 citations
5. Explainability for Large Language Models: A SurveyACM Transactions on Intelligent Systems and TechnologyHaiyan 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 PromptingConference on Neural Information Processing Systems (NeurIPS)Miles Turpin, Julian Michael, Ethan Perez, et al.2023 · 586 citations
7. Large Language Models Can Be Easily Distracted by Irrelevant ContextarXiv:2302.00093Freda Shi, Xinyun Chen, Kanishka Misra, et al.2023 · 534 citations
8. Large Language Models as OptimizersarXiv:2309.03409Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.2023 · 413 citations
9. Larger language models do in-context learning differentlyarXiv:2303.03846Jerry Wei, Jason Wei, Yi Tay, et al.2023 · 285 citations
10. Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What MattersAnnual Meeting of the Association for Computational Linguistics (ACL)Boshi Wang, Sewon Min, Xiang Deng, et al.2023 · 272 citations
11. Measuring Faithfulness in Chain-of-Thought ReasoningarXiv:2307.13702Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.2023 · 262 citations
12. Language Models can Solve Computer TasksConference on Neural Information Processing Systems (NeurIPS)Geunwoo Kim, Pierre Baldi, Stephen McAleer2023 · 249 citations
13. ToolQA: A Dataset for LLM Question Answering with External ToolsConference on Neural Information Processing Systems (NeurIPS)Yuchen Zhuang, Yue Yu, Kuan Wang, et al.2023 · 182 citations
14. Promptbreeder: Self-Referential Self-Improvement Via Prompt EvolutionarXiv:2309.16797Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al.2023 · 174 citations
15. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningarXiv:2409.12183Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.2024 · 138 citations
16. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and FuturearXiv:2309.15402Zheng Chu, Jingchang Chen, Qianglong Chen, et al.2023 · 125 citations
17. The Impact of Reasoning Step Length on Large Language ModelsFindings of the Association for Computational LinguisticsMingyu Jin, Qinkai Yu, Dong Shu, et al.2024 · 119 citations
18. Teaching Algorithmic Reasoning via In-context LearningarXiv:2211.09066Hattie Zhou, Azade Nova, Hugo Larochelle, et al.2022 · 96 citations
19. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
20. What Algorithms can Transformers Learn? A Study in Length GeneralizationarXiv:2310.16028Hattie Zhou, Arwen Bradley, Etai Littwin, et al.2023 · 67 citations
21. Meta-Prompting: Enhancing Language Models with Task-Agnostic ScaffoldingarXiv:2401.12954Mirac Suzgun, Adam Tauman Kalai2024 · 62 citations
22. Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature AttributionsarXiv:2307.13339Skyler Wu, Eric Meng Shen, Charumathi Badrinath, et al.2023 · 30 citations
23. How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and ActivationFindings of the Association for Computational LinguisticsHao 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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