--- title: "Who cites Are NLP Models really able to Solve Simple Math…" description: "592 papers of the Elamur Research library cite «Are NLP Models really able to Solve Simple Math Word Problems?», found in their bibliographies · page 1…" canonical: "https://research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Are NLP Models really able to Solve Simple Math…](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/) / cited by # Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» 592 papers of our library cite it (of the 140884 whose bibliographies we read) · 49 with a page here, the most cited first · page 1 of 2 · [← the paper](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/) **1** [2](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/page/2/) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/) — Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. · 2022 · 11 278 citations - [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) — Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al. · 2022 · 3 780 citations - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/) — Xuezhi Wang, Jason Wei, Dale Schuurmans, et al. · 2022 · 3 035 citations - [A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) — Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · 2023 · 2 847 citations - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/) — Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al. · 2023 · 2 020 citations - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) — Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al. · 2022 · 1 695 citations - [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) — Humza Naveed, Asad Ullah Khan, Shi Qiu, et al. · 2023 · 1 286 citations - [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/) — Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al. · 2023 · 818 citations - [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) — Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al. · 2023 · 806 citations - [Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) — Jie Huang, Kevin Chen-Chuan Chang · 2023 · 775 citations - [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/) — Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al. · 2023 · 684 citations - [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/) — Shayne Longpre, Le Hou, Tu Vu, et al. · 2023 · 590 citations - [Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) — Jiaxin Huang, Shixiang Gu, Le Hou, et al. · 2023 · 560 citations - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/) — Freda Shi, Xinyun Chen, Kanishka Misra, et al. · 2023 · 525 citations - [LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://md.research.elamur.ai/free/en/papers/hu-2023-llm-adapters-adapter-family-parameter/) — Zhiqiang Hu, Lei Wang, Yihuai Lan, et al. · 2023 · 398 citations - [Faithful Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lyu-2023-faithful-chain-thought-reasoning/) — Qing Lyu, Shreya Havaldar, Adam Stein, et al. · 2023 · 345 citations - [UL2: Unifying Language Learning Paradigms](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/) — Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al. · 2022 · 334 citations - [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/) — Zhibin Gou, Zhihong Shao, Yeyun Gong, et al. · 2023 · 315 citations - [Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2023-chameleon-plug-play-compositional-reasoning/) — Pan Lu, Baolin Peng, Hao Cheng, et al. · 2023 · 311 citations - [Large Language Models are Better Reasoners with Self-Verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/) — Yixuan Weng, Minjun Zhu, Fei Xia, et al. · 2023 · 302 citations - [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/) — Xiang Yue, Xingwei Qu, Ge Zhang, et al. · 2023 · 288 citations - [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/) — Janice Ahn, Rishu Verma, Renze Lou, et al. · 2024 · 279 citations - [Scaling Relationship on Learning Mathematical Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/) — Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al. · 2023 · 252 citations - [Language Models can Solve Computer Tasks](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/) — Geunwoo Kim, Pierre Baldi, Stephen McAleer · 2023 · 239 citations - [ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving](https://md.research.elamur.ai/free/en/papers/gou-2023-tora-tool-integrated-reasoning-agent/) — Zhibin Gou, Zhihong Shao, Yeyun Gong, et al. · 2023 · 222 citations **1** [2](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/page/2/)