--- title: "Who cites How well do Large Language — 67 citing papers" description: "67 papers of the Elamur Research library cite «How well do Large Language Models perform in Arithmetic tasks?», found in their…" canonical: "https://research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [How well do Large Language Models perform in…](https://md.research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/) / cited by # Who cites «How well do Large Language Models perform in Arithmetic tasks?» 67 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← back to «How well do Large Language Models perform in Arithmetic tasks?»](https://md.research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/) 1. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-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/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations* 2. **[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* 3. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/) 4. **[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* 5. **[Large Language Models Are Zero-Shot Time Series Forecasters](https://md.research.elamur.ai/free/en/papers/gruver-2023-large-language-models-zero-shot/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nate Gruver, Marc Finzi, Shikai Qiu, et al.*2023 · 268 citations* 6. [**Scaling Relationship on Learning Mathematical Reasoning with Large Language Models arXiv:2308.01825**Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/) 7. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) 8. [**RRHF: Rank Responses to Align Language Models with Human Feedback without tears arXiv:2304.05302**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 201 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-rrhf-rank-responses-align-language/) 9. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/) 10. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/) 11. [**Large language models for education: A survey and outlook**Wang*2024 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-large-language-models-education-survey/) 12. [**MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use arXiv:2310.03128**Yue Huang, Jiawen Shi, Yuan Li, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-metatool-benchmark-large-language-models/) 13. **[Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-contrast-better-reflection-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Zhang, Yongliang Shen, Linjuan Wu, et al.*2024 · 75 citations* 14. **[MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark](https://md.research.elamur.ai/free/en/papers/liu-2024-mathbench-evaluating-theory-application-proficiency/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongwei Liu, Zilong Zheng, Yuxuan Qiao, et al.*2024 · 70 citations* 15. [**Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks arXiv:2305.14201**Tiedong Liu, Bryan Kian Hsiang Low*2023 · 65 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-goat-fine-tuned-llama-outperforms/) 16. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/) 17. [**ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline arXiv:2404.02893**Yifan Xu, Xiao Liu, Xinghan Liu, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-chatglm-math-improving-math/) 18. [**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/) 19. **[ICLEval: Evaluating In-Context Learning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-icleval-evaluating-context-learning-ability/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Wentong Chen, Yankai Lin, ZhenHao Zhou, et al.*2025 · 7 citations* 20. **[VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/ma-2026-visaidmath-benchmarking-visual-aided-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingkun Ma, Runzhe Zhan, Yang Li, et al.*2026 · 5 citations* 21. **[MATH-IDN: A Multilingual Mathematical Problem Solving Dataset Featuring Local Languages in Indonesia](https://md.research.elamur.ai/free/en/papers/xiao-2026-math-idn-multilingual-mathematical-problem/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiao Xiao, Iftitahu Ni'mah, Yuyun Wabula, et al.*2026* 22. **[PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models](https://md.research.elamur.ai/free/en/papers/ouyang-2026-pyramathbench-evaluating-improving-mathematical-capability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zetian Ouyang, Linlin Wang, Gerard de Melo, et al.*2026* 45 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/) — How well do Large Language Models perform in Arithmetic tasks?