--- title: "Who cites Iteratively Prompt Pre-trained — 71 citing papers" description: "71 papers of the Elamur Research library cite «Iteratively Prompt Pre-trained Language Models for Chain of Thought», found in their…" canonical: "https://research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Iteratively Prompt Pre-trained Language Models…](https://md.research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/) / cited by # Who cites «Iteratively Prompt Pre-trained Language Models for Chain of Thought» 71 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «Iteratively Prompt Pre-trained Language Models for Chain of Thought»](https://md.research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/) 1. [**Least-to-most prompting enables complex reasoning in large language models***2022 · 1,165 citations*](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/) 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. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations* 5. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/) 6. [**Multimodal Chain-of-Thought Reasoning in Language Models arXiv:2302.00923**Zhuosheng Zhang, Aston Zhang, Mu Li, et al.*2023 · 308 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-multimodal-chain-thought-reasoning-language/) 7. [**MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning arXiv:2309.05653**Xiang Yue, Xingwei Qu, Ge Zhang, et al.*2023 · 295 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/) 8. [**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/) 9. **[Self-Evaluation Guided Beam Search for Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 149 citations* 10. [**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/) 11. **[Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://md.research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yusen Zhang, Ruoxi Sun, Yanfei Chen, et al.*2024 · 104 citations* 12. **[Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate](https://md.research.elamur.ai/free/en/papers/xiong-2023-examining-inter-consistency-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Xiong, Xiao Ding, Yixin Cao, et al.*2023 · 94 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. **[Task Contamination: Language Models May Not Be Few-Shot Anymore](https://md.research.elamur.ai/free/en/papers/li-2023-task-contamination-language-models-may/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Changmao Li, Jeffrey Flanigan*2023 · 89 citations* 15. **[DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents](https://md.research.elamur.ai/free/en/papers/nair-2023-dera-enhancing-large-language-model/) [Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC](https://md.research.elamur.ai/free/en/venues/clinicalnlp/)**Varun Nair, Elliot Schumacher, Geoffrey Tso, et al.*2023 · 55 citations* 16. [**Self-Verification Improves Few-Shot Clinical Information Extraction arXiv:2306.00024 · IMLH 2023**Zelalem Gero, Chandan Singh, Hao Cheng, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/gero-2023-self-verification-improves-few-shot/) 17. **[NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes](https://md.research.elamur.ai/free/en/papers/fan-2024-nphardeval-dynamic-benchmark-reasoning-ability/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lizhou Fan, Wenyue Hua, Lingyao Li, et al.*2024 · 47 citations* 18. **[Knowledge Boundary of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/li-2025-knowledge-boundary-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Moxin Li, Yong Zhao, Wenxuan Zhang, et al.*2025 · 30 citations* 19. **[CAP: Controllable Alignment Prompting for Unlearning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-cap-controllable-alignment-prompting-unlearning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaokun Wang, Jinyu Guo, Jingwen Pu, et al.*2026* 52 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/) — Iteratively Prompt Pre-trained Language Models for Chain of Thought