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# Who cites «Solving General Arithmetic Word Problems» · page 2

313 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/roy-2015-solving-general-arithmetic-word-problems/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/roy-2015-solving-general-arithmetic-word-problems/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/roy-2015-solving-general-arithmetic-word-problems-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/roy-2015-solving-general-arithmetic-word-problems-4/)

1. [Re-Reading Improves Reasoning in Large Language Models](https://aclanthology.org/2024.emnlp-main.871/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
2. [ReFT: Representation Finetuning for Language Models](https://arxiv.org/abs/2404.03592) *— 2024 · arXiv:2404.03592*
3. [Reasoning in Flux: Enhancing Large Language Models Reasoning through Uncertainty-aware Adaptive Guidance](https://aclanthology.org/2024.acl-long.131/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models](https://aclanthology.org/2024.findings-acl.209/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
5. [SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://arxiv.org/abs/2401.07950) *— 2024 · arXiv:2401.07950*
6. [Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models](https://aclanthology.org/2024.emnlp-main.139/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
7. [Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning](https://aclanthology.org/2024.acl-long.643/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?](https://aclanthology.org/2024.findings-emnlp.980/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
9. [Sparse Matrix in Large Language Model Fine-tuning](https://arxiv.org/abs/2405.15525) *— 2024 · arXiv:2405.15525*
10. [Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models](https://aclanthology.org/2024.acl-long.707/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [The Impact of Reasoning Step Length on Large Language Models](https://aclanthology.org/2024.findings-acl.108/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
12. [Towards A Unified View of Answer Calibration for Multi-Step Reasoning](https://aclanthology.org/2024.nlrse-1.3/) *— 2024 · Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)*
13. [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://arxiv.org/abs/2402.01622) *— 2024 · arXiv:2402.01622*
14. [Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information](https://aclanthology.org/2024.findings-emnlp.388/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
15. [Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://arxiv.org/abs/2402.05376) *— 2024 · arXiv:2402.05376*
16. [面向小规模大语言模型推理优化的推理路径排序方法(A Reasoning Paths Ranking Method for Reasoning Optimization of Small-scale Large Language Models)](https://aclanthology.org/2024.ccl-1.72/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
17. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
18. [A Survey of Deep Learning for Mathematical Reasoning](https://aclanthology.org/2023.acl-long.817/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) *— 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)*
20. [ARB: Advanced Reasoning Benchmark for Large Language Models](https://arxiv.org/abs/2307.13692) *— 2023 · arXiv:2307.13692*
21. [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://arxiv.org/abs/2310.03710) *— 2023 · arXiv:2310.03710*
22. [Are Language Models Worse than Humans at Following Prompts? It's Complicated](https://aclanthology.org/2023.findings-emnlp.514/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
23. [Auto-Regressive Next-Token Predictors are Universal Learners](https://arxiv.org/abs/2309.06979) *— 2023 · arXiv:2309.06979*
24. [Batch Prompting: Efficient Inference with Large Language Model APIs](https://aclanthology.org/2023.emnlp-industry.74/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track*
25. [Better Zero-Shot Reasoning with Role-Play Prompting](https://arxiv.org/abs/2308.07702) *— 2023 · arXiv:2308.07702*
26. [Better Zero-Shot Reasoning with Self-Adaptive Prompting](https://aclanthology.org/2023.findings-acl.216/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
27. [Chain of Thought Prompting Elicits Knowledge Augmentation](https://aclanthology.org/2023.findings-acl.408/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
28. [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://arxiv.org/abs/2310.00280) *— 2023 · arXiv:2310.00280*
29. [DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning](https://arxiv.org/abs/2310.02954) *— 2023 · arXiv:2310.02954*
30. [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) *— 2023 · ICLR 2023 (arXiv 2210.02406)*
31. [Deductive Verification of Chain-of-Thought Reasoning](https://arxiv.org/abs/2306.03872) *— 2023 · arXiv:2306.03872*
32. [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*
33. [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*
34. [DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks](https://arxiv.org/abs/2309.17167) *— 2023 · arXiv:2309.17167*
35. [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*
36. [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://arxiv.org/abs/2305.19118) *— 2023 · arXiv:2305.19118*
37. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
38. [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*
39. [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)*
40. [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)*
41. [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*
42. [GRACE: Discriminator-Guided Chain-of-Thought Reasoning](https://aclanthology.org/2023.findings-emnlp.1022/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
43. [Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models](https://arxiv.org/abs/2310.06692) *— 2023 · arXiv:2310.06692*
44. [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*
45. [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*
46. [Introduction to Mathematical Language Processing: Informal Proofs, Word Problems, and Supporting Tasks](https://aclanthology.org/2023.tacl-1.66/) *— 2023*
47. [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
48. [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*
49. [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) *— 2023 · arXiv:2303.17491*
50. [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*
51. [Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners](https://arxiv.org/abs/2305.14825) *— 2023 · arXiv:2305.14825*
52. [Large Language Models as Optimizers](https://arxiv.org/abs/2309.03409) *— 2023 · arXiv:2309.03409*
53. [Learning to Reason and Memorize with Self-Notes](https://arxiv.org/abs/2305.00833) *— 2023 · arXiv:2305.00833*
54. [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*
55. [Let''s reward step by step: Step-Level reward model as the Navigators for Reasoning](https://arxiv.org/abs/2310.10080) *— 2023 · arXiv:2310.10080*
56. [Limitations of Language Models in Arithmetic and Symbolic Induction](https://aclanthology.org/2023.acl-long.516/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://arxiv.org/abs/2309.05653) *— 2023 · arXiv:2309.05653*
58. [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)*
59. [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)*
60. [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*
61. [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*
62. [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)*
63. [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://arxiv.org/abs/2304.09797) *— 2023 · arXiv:2304.09797*
64. [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://arxiv.org/abs/2309.16797) *— 2023 · arXiv:2309.16797*
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. [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*
67. [Solving Math Word Problems via Cooperative Reasoning induced Language Models](https://aclanthology.org/2023.acl-long.245/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
68. [Symbolic Chain-of-Thought Distillation: Small Models Can Also ``Think'''' Step-by-Step](https://aclanthology.org/2023.acl-long.150/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [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*
70. [Teaching Arithmetic to Small Transformers](https://arxiv.org/abs/2307.03381) *— 2023 · arXiv:2307.03381*
71. [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*
72. [Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration](https://arxiv.org/abs/2311.08152) *— 2023 · arXiv:2311.08152*
73. [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*
74. [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)*
75. [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://arxiv.org/abs/2308.09583) *— 2023 · arXiv:2308.09583*
76. [World Models for Math Story Problems](https://aclanthology.org/2023.findings-acl.579/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
77. [AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model](https://arxiv.org/abs/2208.01448) *— 2022 · arXiv:2208.01448*
78. [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*
79. [Automatic Chain of Thought Prompting in Large Language Models](https://arxiv.org/abs/2210.03493) *— 2022 · arXiv:2210.03493*
80. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) *— 2022 · arXiv:2201.11903*
81. [Complexity-Based Prompting for Multi-Step Reasoning](https://arxiv.org/abs/2210.00720) *— 2022 · arXiv:2210.00720*
82. [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*
83. [Investigating Math Word Problems using Pretrained Multilingual Language Models](https://aclanthology.org/2022.mathnlp-1.2/) *— 2022 · Proceedings of the 1st Workshop on Mathematical Natural Language Processing (MathNLP)*
84. [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*
85. [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) *— 2022 · arXiv:2211.01910*
86. [Large language models are zero-shot reasoners](https://arxiv.org/abs/2205.11916) *— 2022*
87. [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://md.mova.elamur.ai/free/en/docs/papers/jie-2022-learning-reason-deductively-math-word/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Mind''s Eye: Grounded Language Model Reasoning through Simulation](https://arxiv.org/abs/2210.05359) *— 2022 · arXiv:2210.05359*
89. [NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks](https://aclanthology.org/2022.acl-long.246/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
90. [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://arxiv.org/abs/2211.12588) *— 2022 · arXiv:2211.12588*
91. [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) *— 2022 · arXiv:2212.09597*
92. [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) *— 2022 · arXiv:2203.11171*
93. [Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers](https://arxiv.org/abs/2205.15683) *— 2022 · arXiv 2205.15683*
94. [A Bottom-Up DAG Structure Extraction Model for Math Word Problems](https://ojs.aaai.org/index.php/AAAI/article/view/16075) *— 2021 · AAAI 2021*
95. [Generate & Rank: A Multi-task Framework for Math Word Problems](https://aclanthology.org/2021.findings-emnlp.195/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
96. [Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning](https://aclanthology.org/2021.emnlp-main.272/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
97. [Mapping probability word problems to executable representations](https://aclanthology.org/2021.emnlp-main.294/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
98. [Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints](https://aclanthology.org/2021.emnlp-main.484/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
99. [Math Word Problem Solving with Explicit Numerical Values](https://aclanthology.org/2021.acl-long.455/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
100. [NaturalProofs: Mathematical Theorem Proving in Natural Language](https://arxiv.org/abs/2104.01112) *— 2021 · arXiv:2104.01112*

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