---
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; the most…"
canonical: "https://research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/cited-by/"
---
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# Who cites «Are NLP Models really able to Solve Simple Math Word Problems?»

592 papers of our library cite it (of the 140,884 whose bibliographies we read) · 51 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/)

1. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,278 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
2. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,780 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
3. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,035 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
4. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
5. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
6. [**Toolformer: Language Models Can Teach Themselves to Use Tools**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,020 citations*](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/)
7. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
8. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
9. [**Is ChatGPT a General-Purpose Natural Language Processing Task Solver?**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 818 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/)
10. [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
11. [**Towards Reasoning in Large Language Models: A Survey**Jie Huang, Kevin Chen-Chuan Chang*2023 · 775 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/)
12. [**Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 684 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/)
13. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
14. [**Large Language Models Can Self-Improve**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 560 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/)
15. [**Large Language Models Can Be Easily Distracted by Irrelevant Context**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 525 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
16. [**LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models**Zhiqiang Hu, Lei Wang, Yihuai Lan, et al.*2023 · 398 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-llm-adapters-adapter-family-parameter/)
17. [**Faithful Chain-of-Thought Reasoning**Qing Lyu, Shreya Havaldar, Adam Stein, et al.*2023 · 345 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-faithful-chain-thought-reasoning/)
18. [**UL2: Unifying Language Learning Paradigms**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 334 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
19. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
20. [**Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models**Pan Lu, Baolin Peng, Hao Cheng, et al.*2023 · 311 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-chameleon-plug-play-compositional-reasoning/)
21. [**Large Language Models are Better Reasoners with Self-Verification**Yixuan Weng, Minjun Zhu, Fei Xia, et al.*2023 · 302 citations*](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/)
22. [**MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning**Xiang Yue, Xingwei Qu, Ge Zhang, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/)
23. [**Large Language Models for Mathematical Reasoning: Progresses and Challenges**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 279 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/)
24. [**Scaling Relationship on Learning Mathematical Reasoning with Large Language Models**Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/)
25. [**Language Models can Solve Computer Tasks**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/)
26. [**ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 222 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-tora-tool-integrated-reasoning-agent/)
27. [**Distilling Reasoning Capabilities into Smaller Language Models**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
28. [**Making Language Models Better Reasoners with Step-Aware Verifier**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/)
29. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
30. [**PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/)
31. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
32. [**LILA: A Unified Benchmark for Mathematical Reasoning**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/)
33. [**Generating Sequences by Learning to Self-Correct**Sean Welleck, Ximing Lu, Peter West, et al.*2022 · 170 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/)
34. [**Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution**Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/fernando-2023-promptbreeder-self-referential-self-improvement/)
35. [**Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/)
36. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
37. [**DataComp-LM: In search of the next generation of training sets for language models**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 156 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/)
38. [**TheoremQA: A Theorem-driven Question Answering Dataset**Wenhu Chen, Ming Yin, Max Ku, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/)
39. [**REFINER: Reasoning Feedback on Intermediate Representations**Debjit Paul, Mete Ismayilzada, Maxime Peyrard, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/paul-2023-refiner-reasoning-feedback-intermediate-representations/)
40. [**Self-Evaluation Guided Beam Search for Reasoning**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/)
41. [**ReFT: Reasoning with Reinforced Fine-Tuning**Luong Trung, Xinbo Zhang, Zhanming Jie, et al.*2024 · 131 citations*](https://md.research.elamur.ai/free/en/papers/trung-2024-reft-reasoning-reinforced-fine-tuning/)
42. [**The Impact of Reasoning Step Length on Large Language Models**Mingyu Jin, Qinkai Yu, Dong Shu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/)
43. [**Orca-Math: Unlocking the potential of SLMs in Grade School Math**Arindam Mitra, Hamed Khanpour, Corby Rosset, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-orca-math-unlocking-potential-slms/)
44. [**GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers**Qintong Li, Leyang Cui, Xueliang Zhao, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-gsm-plus-comprehensive-benchmark-evaluating/)
45. [**Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction**Zhanming Jie, Jierui Li, Wei Lu*2022 · 102 citations*](https://md.research.elamur.ai/free/en/papers/jie-2022-learning-reason-deductively-math-word/)
46. [**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning**Ke Wang, Houxing Ren, Aojun Zhou, et al.*2023 · 98 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/)
47. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
48. [**Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data**Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, et al.*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/akhtar-2023-exploring-numerical-reasoning-capabilities-language/)
49. [**ArMATH: a Dataset for Solving Arabic Math Word Problems**Reem Alghamdi, Zhenwen Liang, Xiangliang Zhang*2022 · 17 citations*](https://md.research.elamur.ai/free/en/papers/alghamdi-2022-armath-dataset-solving-arabic-math/)
50. [**Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/)
51. [**Cutting Through the Noise: Boosting LLM Performance on Math Word Problems**Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/)

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

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