---
title: "Who cites Rationale-Augmented Ensembles in Language Models"
description: "73 papers of the Elamur Research library cite «Rationale-Augmented Ensembles in Language Models», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/wang-2022-rationale-augmented-ensembles-language-models/cited-by/"
---
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# Who cites «Rationale-Augmented Ensembles in Language Models»

73 papers of our library cite it (of the 149,439 whose bibliographies we read) · 29 with a page here, the most cited first · [← back to «Rationale-Augmented Ensembles in Language Models»](https://md.research.elamur.ai/free/en/papers/wang-2022-rationale-augmented-ensembles-language-models/)

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. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-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/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
4. **[Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 828 citations*
5. [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 786 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
6. **[Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 575 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. [**Language Models are Multilingual Chain-of-Thought Reasoners arXiv:2210.03057**Freda Shi, Mirac Suzgun, Markus Freitag, et al.*2022 · 494 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/)
9. [**Complexity-Based Prompting for Multi-Step Reasoning arXiv:2210.00720**Yao Fu, Hao Peng, Ashish Sabharwal, et al.*2022 · 345 citations*](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/)
10. [**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/)
11. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines arXiv:2310.03714**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
12. [**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/)
13. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP arXiv:2212.14024**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 222 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
14. [**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/)
15. **[Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.*2023 · 140 citations*
16. [**Progressive-Hint Prompting Improves Reasoning in Large Language Models arXiv:2304.09797**Chuanyang Zheng, Zhengying Liu, Enze Xie, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-progressive-hint-prompting-improves-reasoning/)
17. **[Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://md.research.elamur.ai/free/en/papers/shum-2023-automatic-prompt-augmentation-selection-chain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kashun Shum, Shizhe Diao, Tong Zhang*2023 · 128 citations*
18. **[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*
19. [**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/)
20. **[Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://md.research.elamur.ai/free/en/papers/wang-2024-boosting-language-models-reasoning-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianing Wang, Qiushi Sun, Xiang Li, et al.*2024 · 94 citations*
21. **[Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning](https://md.research.elamur.ai/free/en/papers/tafjord-2022-entailer-answering-questions-faithful/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark*2022 · 71 citations*
22. **[Aligning Large and Small Language Models via Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ranaldi-2024-aligning-large-small-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Leonardo Ranaldi, Andre Freitas*2024 · 70 citations*
23. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
24. [**In-Context Principle Learning from Mistakes arXiv:2402.05403**Tianjun Zhang, Aman Madaan, Luyu Gao, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-context-principle-learning-mistakes/)
25. **[GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering](https://md.research.elamur.ai/free/en/papers/luo-2026-gcot-decoding-unlocking-deep-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guanran Luo, Wentao Qiu, Zhongquan Jian, et al.*2026 · 6 citations*
26. **[Task Matters: Knowledge Requirements Shape LLM Responses to Context–Memory Conflict](https://md.research.elamur.ai/free/en/papers/sun-2026-task-matters-knowledge-requirements-shape/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaiser Sun, Fan Bai, Mark Dredze*2026 · 3 citations*
27. **[H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs](https://md.research.elamur.ai/free/en/papers/tekin-2026-h3fusion-helpful-harmless-honest-fusion/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Selim Furkan Tekin, Fatih Ilhan, Sihao Hu, et al.*2026 · 1 citation*
28. **[A Single Model Ensemble Framework for Neural Machine Translation Using Pivot Translation](https://md.research.elamur.ai/free/en/papers/oh-2026-single-model-ensemble-framework-neural/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Seokjin Oh, Keonwoong Noh, Woohwan Jung*2026*
29. **[GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-gsm-noise-exploring-enhancing-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengxin Zhang, Chengyu Huang, Xufu Liu, et al.*2026*

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

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