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# Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 4

592 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/patel-2021-are-nlp-models-really-able/)

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1. [Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models](https://arxiv.org/abs/2304.09842) *— 2023 · arXiv:2304.09842*
2. [Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset](https://aclanthology.org/2023.findings-emnlp.427/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
3. [Contrastive Chain-of-Thought Prompting](https://arxiv.org/abs/2311.09277) *— 2023 · arXiv:2311.09277*
4. [Contrastive Decoding Improves Reasoning in Large Language Models](https://arxiv.org/abs/2309.09117) *— 2023 · arXiv:2309.09117*
5. [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://arxiv.org/abs/2310.00280) *— 2023 · arXiv:2310.00280*
6. [DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning](https://arxiv.org/abs/2310.02954) *— 2023 · arXiv:2310.02954*
7. [Defining a New NLP Playground](https://aclanthology.org/2023.findings-emnlp.799/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
8. [Democratizing Reasoning Ability: Tailored Learning from Large Language Model](https://aclanthology.org/2023.emnlp-main.120/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
9. [Design of Chain-of-Thought in Math Problem Solving](https://arxiv.org/abs/2309.11054) *— 2023 · arXiv:2309.11054*
10. [DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models](https://aclanthology.org/2023.emnlp-main.501/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
11. [Distilling Reasoning Capabilities into Smaller Language Models](https://aclanthology.org/2023.findings-acl.441/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
12. [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://aclanthology.org/2023.findings-acl.507/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
13. [DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks](https://arxiv.org/abs/2309.17167) *— 2023 · arXiv:2309.17167*
14. [Dynamic Voting for Efficient Reasoning in Large Language Models](https://aclanthology.org/2023.findings-emnlp.203/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
15. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
16. [Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication](https://aclanthology.org/2023.emnlp-main.936/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
17. [Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting](https://aclanthology.org/2023.emnlp-main.41/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
18. [Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data](https://aclanthology.org/2023.findings-emnlp.1028/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
19. [Expression Syntax Information Bottleneck for Math Word Problems](https://arxiv.org/abs/2310.15664) *— 2023 · arXiv:2310.15664*
20. [FERMAT: An Alternative to Accuracy for Numerical Reasoning](https://aclanthology.org/2023.acl-long.838/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Faithful Chain-of-Thought Reasoning](https://aclanthology.org/2023.ijcnlp-main.20/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems](https://arxiv.org/abs/2310.01991) *— 2023 · arXiv:2310.01991*
23. [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://arxiv.org/abs/2303.04129) *— 2023 · arXiv:2303.04129*
24. [From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning](https://arxiv.org/abs/2304.07995) *— 2023 · arXiv:2304.07995*
25. [GRACE: Discriminator-Guided Chain-of-Thought Reasoning](https://aclanthology.org/2023.findings-emnlp.1022/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
26. [Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models](https://arxiv.org/abs/2310.06692) *— 2023 · arXiv:2310.06692*
27. [Getting MoRE out of Mixture of Language Model Reasoning Experts](https://aclanthology.org/2023.findings-emnlp.552/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
28. [Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT](https://arxiv.org/abs/2304.11116) *— 2023 · arXiv:2304.11116*
29. [GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach](https://arxiv.org/abs/2308.09267) *— 2023 · arXiv:2308.09267*
30. [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://arxiv.org/abs/2304.13712) *— 2023 · arXiv:2304.13712*
31. [How Many Demonstrations Do You Need for In-context Learning?](https://aclanthology.org/2023.findings-emnlp.745/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
32. [Improving Mathematics Tutoring With A Code Scratchpad](https://aclanthology.org/2023.bea-1.2/) *— 2023 · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)*
33. [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
34. [It Ain''t Over: A Multi-aspect Diverse Math Word Problem Dataset](https://aclanthology.org/2023.emnlp-main.927/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
35. [KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models](https://aclanthology.org/2023.findings-emnlp.631/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
36. [LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://aclanthology.org/2023.emnlp-main.319/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
37. [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) *— 2023 · arXiv:2303.17491*
38. [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) *— 2023 · arXiv:2302.00093*
39. [Large Language Models Can Self-Improve](https://aclanthology.org/2023.emnlp-main.67/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
40. [Large Language Models are Better Reasoners with Self-Verification](https://aclanthology.org/2023.findings-emnlp.167/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
41. [Learning From Mistakes Makes LLM Better Reasoner](https://arxiv.org/abs/2310.20689) *— 2023 · arXiv:2310.20689*
42. [Learning Multi-Step Reasoning by Solving Arithmetic Tasks](https://aclanthology.org/2023.acl-short.106/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
43. [Learning by Analogy: Diverse Questions Generation in Math Word Problem](https://aclanthology.org/2023.findings-acl.705/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
44. [Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation](https://aclanthology.org/2023.emnlp-main.889/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
45. [Let''s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs](https://aclanthology.org/2023.emnlp-main.761/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
46. [Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning](https://aclanthology.org/2023.findings-acl.668/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
47. [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://arxiv.org/abs/2309.05653) *— 2023 · arXiv:2309.05653*
48. [Making Language Models Better Reasoners with Step-Aware Verifier](https://aclanthology.org/2023.acl-long.291/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Making Presentation Math Computable: A Context-Sensitive Approach for Translating LaTeX to Computer Algebra Systems](https://link.springer.com/book/10.1007/978-3-658-40473-4) *— 2023 · monograph*
50. [Math Word Problem Solving by Generating Linguistic Variants of Problem Statements](https://aclanthology.org/2023.acl-srw.49/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
51. [MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning](https://arxiv.org/abs/2310.03731) *— 2023 · arXiv:2310.03731*
52. [MathPile: A Billion-Token-Scale Pretraining Corpus for Math](https://arxiv.org/abs/2312.17120) *— 2023 · arXiv:2312.17120*
53. [Mathematics, word problems, common sense, and artificial intelligence](https://arxiv.org/abs/2301.09723) *— 2023 · arXiv:2301.09723*
54. [Mixed Distillation Helps Smaller Language Model Better Reasoning](https://arxiv.org/abs/2312.10730) *— 2023 · arXiv:2312.10730*
55. [Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models](https://arxiv.org/abs/2305.14705) *— 2023 · arXiv:2305.14705*
56. [NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports](https://aclanthology.org/2023.emnlp-main.1041/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
57. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://arxiv.org/abs/2309.15402) *— 2023 · arXiv:2309.15402*
58. [Neural Machine Translation for Mathematical Formulae](https://aclanthology.org/2023.acl-long.645/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [Non-Autoregressive Math Word Problem Solver with Unified Tree Structure](https://aclanthology.org/2023.emnlp-main.199/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
60. [Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts](https://aclanthology.org/2023.emnlp-main.169/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
61. [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://aclanthology.org/2023.acl-long.147/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://arxiv.org/abs/2304.09797) *— 2023 · arXiv:2304.09797*
63. [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://arxiv.org/abs/2309.16797) *— 2023 · arXiv:2309.16797*
64. [PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection](https://aclanthology.org/2023.findings-emnlp.216/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
65. [R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context](https://aclanthology.org/2023.findings-emnlp.114/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
66. [REFINER: Reasoning Feedback on Intermediate Representations](https://arxiv.org/abs/2304.01904) *— 2023 · arXiv:2304.01904*
67. [Reasoning in Large Language Models Through Symbolic Math Word Problems](https://aclanthology.org/2023.findings-acl.364/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
68. [Retrieving Multimodal Information for Augmented Generation: A Survey](https://aclanthology.org/2023.findings-emnlp.314/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
69. [Scaling Relationship on Learning Mathematical Reasoning with Large Language Models](https://arxiv.org/abs/2308.01825) *— 2023 · arXiv:2308.01825*
70. [Self-Evaluation Guided Beam Search for Reasoning](https://arxiv.org/abs/2305.00633) *— 2023 · arXiv:2305.00633*
71. [Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement](https://aclanthology.org/2023.findings-emnlp.762/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
72. [SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data](https://aclanthology.org/2023.semeval-1.307/) *— 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)*
73. [TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models](https://aclanthology.org/2023.emnlp-main.711/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
74. [Tab-CoT: Zero-shot Tabular Chain of Thought](https://aclanthology.org/2023.findings-acl.651/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
75. [Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems](https://arxiv.org/abs/2308.05713) *— 2023 · arXiv:2308.05713*
76. [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://arxiv.org/abs/2301.13688) *— 2023 · arXiv:2301.13688*
77. [TheoremQA: A Theorem-driven Question Answering Dataset](https://aclanthology.org/2023.emnlp-main.489/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
78. [Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning](https://aclanthology.org/2023.findings-emnlp.201/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
79. [TinyGSM: achieving >80% on GSM8k with small language models](https://arxiv.org/abs/2312.09241) *— 2023 · arXiv:2312.09241*
80. [ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving](https://arxiv.org/abs/2309.17452) *— 2023 · arXiv:2309.17452*
81. [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/abs/2302.04761) *— 2023 · arXiv:2302.04761*
82. [Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration](https://arxiv.org/abs/2311.08152) *— 2023 · arXiv:2311.08152*
83. [Towards Reasoning in Large Language Models: A Survey](https://aclanthology.org/2023.findings-acl.67/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
84. [UniMath: A Foundational and Multimodal Mathematical Reasoner](https://aclanthology.org/2023.emnlp-main.440/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
85. [Unleashing the potential of prompt engineering for large language models](https://arxiv.org/abs/2310.14735) *— 2023 · arXiv:2310.14735 · Patterns 6(6) 101260 (2025)*
86. [What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study](https://aclanthology.org/2023.findings-emnlp.101/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
87. [World Models for Math Story Problems](https://aclanthology.org/2023.findings-acl.579/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
88. [WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models](https://arxiv.org/abs/2311.15930) *— 2023 · arXiv:2311.15930*
89. [ArMATH: a Dataset for Solving Arabic Math Word Problems](https://aclanthology.org/2022.lrec-1.37/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
90. [Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions](https://aclanthology.org/2022.emnlp-industry.4/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track*
91. [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://aclanthology.org/2022.emnlp-main.277/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
92. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
93. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) *— 2022 · arXiv:2201.11903*
94. [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://arxiv.org/abs/2209.14610) *— 2022 · arXiv:2209.14610*
95. [Generating Sequences by Learning to Self-Correct](https://arxiv.org/abs/2211.00053) *— 2022 · arXiv:2211.00053*
96. [HAWP: a Dataset for Hindi Arithmetic Word Problem Solving](https://aclanthology.org/2022.lrec-1.373/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
97. [Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://aclanthology.org/2022.findings-emnlp.59/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
98. [Is a Question Decomposition Unit All We Need?](https://aclanthology.org/2022.emnlp-main.302/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
99. [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
100. [Language models show human-like content effects on reasoning tasks](https://arxiv.org/abs/2207.07051) *— 2022 · arXiv:2207.07051*

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