--- title: "Who cites Learning From Mistakes Makes LLM Better Reasoner" description: "59 papers of the Elamur Research library cite «Learning From Mistakes Makes LLM Better Reasoner», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/an-2023-learning-mistakes-makes-llm-better/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/an-2023-learning-mistakes-makes-llm-better/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Learning From Mistakes Makes LLM Better Reasoner](https://md.research.elamur.ai/free/en/papers/an-2023-learning-mistakes-makes-llm-better/) / cited by # Who cites «Learning From Mistakes Makes LLM Better Reasoner» 59 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «Learning From Mistakes Makes LLM Better Reasoner»](https://md.research.elamur.ai/free/en/papers/an-2023-learning-mistakes-makes-llm-better/) 1. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/) 2. **[Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 280 citations* 3. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations* 4. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations* 5. [**Understanding the planning of LLM agents: A survey arXiv:2402.02716**Xu Huang, Weiwen Liu, Xiaolong Chen, et al.*2024 · 128 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-understanding-planning-llm-agents-survey/) 6. [**V-STaR: Training Verifiers for Self-Taught Reasoners arXiv:2402.06457**Arian Hosseini, Xingdi Yuan, Nikolay Malkin, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2024-v-star-training-verifiers-self/) 7. [**Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security arXiv:2401.05459**Yuanchun Li, Hao Wen, Weijun Wang, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-personal-llm-agents-insights-survey/) 8. **[Make Your LLM Fully Utilize the Context](https://md.research.elamur.ai/free/en/papers/an-2024-make-llm-fully-utilize-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shengnan An, Zexiong Ma, Zeqi Lin, et al.*2024 · 97 citations* 9. [**Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive arXiv:2402.13228**Arka Pal, Deep Karkhanis, Samuel Dooley, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/pal-2024-smaug-fixing-failure-modes-preference/) 10. [**Common 7B Language Models Already Possess Strong Math Capabilities arXiv:2403.04706**Chen Li, Weiqi Wang, Jingcheng Hu, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-common-7b-language-models-already/) 11. **[Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning](https://md.research.elamur.ai/free/en/papers/tong-2024-can-llms-learn-previous-mistakes/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongqi Tong, Dawei Li, Sizhe Wang, et al.*2024 · 57 citations* 12. **[Vision-Language Models Can Self-Improve Reasoning via Reflection](https://md.research.elamur.ai/free/en/papers/cheng-2025-vision-language-models-can-self/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kanzhi Cheng, Li YanTao, Fangzhi Xu, et al.*2025 · 35 citations* 13. [**Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes On arXiv:2407.08348**Liang Zeng, Liangjun Zhong, Liang Zhao, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-skywork-math-data-scaling-laws/) 14. **[CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios](https://md.research.elamur.ai/free/en/papers/huang-2025-critictool-evaluating-self-critique-capabilities/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shiting Huang, Zhen Fang, Zehui Chen, et al.*2025 · 12 citations* 15. [**SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models arXiv:2408.15565**Dian Yu, Baolin Peng, Ye Tian, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/yu-2024-siam-self-improving-code-assisted/) 16. **[Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2025-self-error-instruct-generalizing-errors/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Erxin Yu, Jing Li, Ming Liao, et al.*2025 · 5 citations* 17. [**Baichuan Alignment Technical Report arXiv:2410.14940**Mingan Lin, Fan Yang, Yanjun Shen, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-baichuan-alignment-technical-report/) 18. **[Mistake Notebook Learning: Batch-Clustered Failures for Training-Free Agent Adaptation](https://md.research.elamur.ai/free/en/papers/su-2026-mistake-notebook-learning-batch-clustered/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuanbo Su, Yingfang Zhang, Hao Luo, et al.*2026 · 2 citations* 19. **[Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection](https://md.research.elamur.ai/free/en/papers/wu-2026-enhancing-long-chain-reasoning-distillation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoyang Wu, Xinze Li, Zhenghao Liu, et al.*2026 · 1 citation* 20. **[Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://md.research.elamur.ai/free/en/papers/xueyun-2026-learning-mistakes-negative-reasoning-samples/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tian Xueyun, MingHua Ma, Bingbing Xu, et al.*2026* 21. **[QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-queueedit-structural-self-correction-sequential/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Taolin Zhang, Haidong Kang, Dongyang Li, et al.*2026* 38 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/an-2023-learning-mistakes-makes-llm-better/) — Learning From Mistakes Makes LLM Better Reasoner