🔗 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»

1. Least-to-most prompting enables complex reasoning in large language models2022 · 1,165 citations
2. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
3. Towards Reasoning in Large Language Models: A SurveyFindings of the Association for Computational LinguisticsJie Huang, Kevin Chen-Chuan Chang2023 · 780 citations
4. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.2022 · 754 citations
5. Augmented Language Models: a SurveyarXiv:2302.07842Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.2023 · 369 citations
6. Multimodal Chain-of-Thought Reasoning in Language ModelsarXiv:2302.00923Zhuosheng Zhang, Aston Zhang, Mu Li, et al.2023 · 308 citations
7. MAmmoTH: Building Math Generalist Models through Hybrid Instruction TuningarXiv:2309.05653Xiang Yue, Xingwei Qu, Ge Zhang, et al.2023 · 295 citations
8. Reasoning with Language Model Prompting: A SurveyarXiv:2212.09597Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.2022 · 161 citations
9. Self-Evaluation Guided Beam Search for ReasoningConference on Neural Information Processing Systems (NeurIPS)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 FuturearXiv:2309.15402Zheng Chu, Jingchang Chen, Qianglong Chen, et al.2023 · 125 citations
11. Chain of Agents: Large Language Models Collaborating on Long-Context TasksConference on Neural Information Processing Systems (NeurIPS)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 DebateFindings of the Association for Computational LinguisticsKai Xiong, Xiao Ding, Yixin Cao, et al.2023 · 94 citations
13. LAMBADA: Backward Chaining for Automated Reasoning in Natural LanguageAnnual Meeting of the Association for Computational Linguistics (ACL)Mehran Kazemi, Najoung Kim, Deepti Bhatia, et al.2023 · 89 citations
14. Task Contamination: Language Models May Not Be Few-Shot AnymoreProceedings of the AAAI Conference on Artificial IntelligenceChangmao Li, Jeffrey Flanigan2023 · 89 citations
15. DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving AgentsWorkshop on Clinical Natural Language Processing (Clinical NLP) @ LRECVarun Nair, Elliot Schumacher, Geoffrey Tso, et al.2023 · 55 citations
16. Self-Verification Improves Few-Shot Clinical Information ExtractionarXiv:2306.00024 · IMLH 2023Zelalem Gero, Chandan Singh, Hao Cheng, et al.2023 · 51 citations
17. NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity ClassesAnnual Meeting of the Association for Computational Linguistics (ACL)Lizhou Fan, Wenyue Hua, Lingyao Li, et al.2024 · 47 citations
18. Knowledge Boundary of Large Language Models: A SurveyAnnual Meeting of the Association for Computational Linguistics (ACL)Moxin Li, Yong Zhao, Wenxuan Zhang, et al.2025 · 30 citations
19. CAP: Controllable Alignment Prompting for Unlearning in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhaokun Wang, Jinyu Guo, Jingwen Pu, et al.2026

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