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# Who cites «Learning to Solve Arithmetic Word Problems with Verb Categorization»

222 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/hosseini-2014-learning-solve-arithmetic-word-problems/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hosseini-2014-learning-solve-arithmetic-word-problems-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hosseini-2014-learning-solve-arithmetic-word-problems-3/)

1. [FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning](https://aclanthology.org/2026.findings-acl.1580/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [G-LoRA: Global-Local Decoupled Low-Rank Adaptation](https://aclanthology.org/2026.findings-acl.1005/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
3. [GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning](https://aclanthology.org/2026.findings-acl.475/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
4. [Localized Low-Rank Adaptation within Clustered Parameter Subspaces](https://aclanthology.org/2026.acl-long.1223/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [TARE: Lightweight Token-Aware Representation Editing for Fine-tuning Transformer-like Models](https://aclanthology.org/2026.acl-long.841/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [TabularMath: Understanding Math Reasoning over Tables with Large Language Models](https://aclanthology.org/2026.findings-acl.759/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
7. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
8. [A Systematic Survey of Automatic Prompt Optimization Techniques](https://aclanthology.org/2025.emnlp-main.1681/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
9. [Advancing Collaborative Debates with Role Differentiation through Multi-Agent Reinforcement Learning](https://aclanthology.org/2025.acl-long.1105/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [BanglaMATH : A Bangla benchmark dataset for testing LLM mathematical reasoning at grades 6, 7, and 8](https://aclanthology.org/2025.mathnlp-main.10/) *— 2025 · Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)*
11. [Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective](https://aclanthology.org/2025.acl-long.1213/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [DenseLoRA: Dense Low-Rank Adaptation of Large Language Models](https://aclanthology.org/2025.acl-long.503/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework](https://aclanthology.org/2025.findings-emnlp.20/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
14. [Evaluating LLMs with Multiple Problems at once](https://aclanthology.org/2025.gem-1.14/) *— 2025 · Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM\texttwosuperior)*
15. [Evaluating Numeracy of Language Models as a Natural Language Inference Task](https://aclanthology.org/2025.findings-naacl.467/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
16. [Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning](https://aclanthology.org/2025.findings-acl.1249/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
17. [Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models](https://aclanthology.org/2025.findings-acl.586/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
18. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
19. [Learning Text Styles: A Study on Transfer, Attribution, and Verification](https://arxiv.org/abs/2507.16530) *— 2025 · arXiv:2507.16530*
20. [Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning](https://aclanthology.org/2025.findings-naacl.383/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
21. [On the Generalization vs Fidelity Paradox in Knowledge Distillation](https://aclanthology.org/2025.findings-acl.923/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
22. [Persona is a Double-Edged Sword: Rethinking the Impact of Role-play Prompts in Zero-shot Reasoning Tasks](https://aclanthology.org/2025.findings-ijcnlp.51/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
23. [Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance](https://aclanthology.org/2025.emnlp-main.1364/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
24. [Propulsion: Steering LLM with Tiny Fine-Tuning](https://aclanthology.org/2025.coling-main.506/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
25. [RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates](https://aclanthology.org/2025.acl-long.1293/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Self-Harmonized Chain of Thought](https://aclanthology.org/2025.naacl-long.53/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
27. [Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster](https://aclanthology.org/2025.emnlp-main.610/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
28. [Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales](https://aclanthology.org/2025.findings-emnlp.413/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
29. [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://arxiv.org/abs/2501.09686) *— 2025 · arXiv:2501.09686*
30. [UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions](https://aclanthology.org/2025.findings-acl.423/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
31. [UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models](https://aclanthology.org/2025.acl-long.575/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning](https://aclanthology.org/2025.emnlp-main.642/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
33. [Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning](https://aclanthology.org/2024.emnlp-main.313/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
34. [Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models](https://aclanthology.org/2024.lrec-main.53/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
35. [Bridging the Gap between Different Vocabularies for LLM Ensemble](https://aclanthology.org/2024.naacl-long.395/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
36. [ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models](https://aclanthology.org/2024.findings-acl.407/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
37. [DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents](https://aclanthology.org/2024.acl-long.852/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models](https://aclanthology.org/2024.findings-naacl.257/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
39. [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://arxiv.org/abs/2401.10480) *— 2024 · arXiv:2401.10480*
40. [Explicit Memory Learning with Expectation Maximization](https://aclanthology.org/2024.emnlp-main.927/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
41. [Exploring Reversal Mathematical Reasoning Ability for Large Language Models](https://aclanthology.org/2024.findings-acl.811/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
42. [Fewer is More: Boosting Math Reasoning with Reinforced Context Pruning](https://aclanthology.org/2024.emnlp-main.758/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
43. [Forward-Backward Reasoning in Large Language Models for Mathematical Verification](https://aclanthology.org/2024.findings-acl.397/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
44. [Improving Language Model Reasoning with Self-motivated Learning](https://aclanthology.org/2024.lrec-main.774/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
45. [Instructing Large Language Models to Identify and Ignore Irrelevant Conditions](https://aclanthology.org/2024.naacl-long.379/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
46. [Interpreting and Improving Large Language Models in Arithmetic Calculation](https://arxiv.org/abs/2409.01659) *— 2024 · arXiv:2409.01659*
47. [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://aclanthology.org/2024.eacl-srw.17/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop*
48. [LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models](https://aclanthology.org/2024.lrec-main.940/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
49. [Making Language Models Better Tool Learners with Execution Feedback](https://aclanthology.org/2024.naacl-long.195/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
50. [Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models](https://aclanthology.org/2024.findings-acl.34/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
51. [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://arxiv.org/abs/2405.12205) *— 2024 · arXiv:2405.12205*
52. [MinT: Boosting Generalization in Mathematical Reasoning via Multi-view Fine-tuning](https://aclanthology.org/2024.lrec-main.988/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
53. [NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models](https://aclanthology.org/2024.findings-acl.848/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
54. [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/abs/2402.14830) *— 2024 · arXiv:2402.14830*
55. [Personality Alignment of Large Language Models](https://arxiv.org/abs/2408.11779) *— 2024 · arXiv:2408.11779*
56. [Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models](https://aclanthology.org/2024.findings-naacl.119/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
57. [RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners](https://aclanthology.org/2024.lrec-main.1183/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
58. [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*
59. [ReFT: Representation Finetuning for Language Models](https://arxiv.org/abs/2404.03592) *— 2024 · arXiv:2404.03592*
60. [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)*
61. [SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://arxiv.org/abs/2401.07950) *— 2024 · arXiv:2401.07950*
62. [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*
63. [Sparse Matrix in Large Language Model Fine-tuning](https://arxiv.org/abs/2405.15525) *— 2024 · arXiv:2405.15525*
64. [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)*
65. [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*
66. [Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://arxiv.org/abs/2402.05376) *— 2024 · arXiv:2402.05376*
67. [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)*
68. [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*
69. [Better Zero-Shot Reasoning with Role-Play Prompting](https://arxiv.org/abs/2308.07702) *— 2023 · arXiv:2308.07702*
70. [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*
71. [Chain of Thought Prompting Elicits Knowledge Augmentation](https://aclanthology.org/2023.findings-acl.408/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
72. [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://arxiv.org/abs/2310.00280) *— 2023 · arXiv:2310.00280*
73. [Deductive Verification of Chain-of-Thought Reasoning](https://arxiv.org/abs/2306.03872) *— 2023 · arXiv:2306.03872*
74. [Defining a New NLP Playground](https://aclanthology.org/2023.findings-emnlp.799/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
75. [Distilling Reasoning Capabilities into Smaller Language Models](https://aclanthology.org/2023.findings-acl.441/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
76. [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://arxiv.org/abs/2305.19118) *— 2023 · arXiv:2305.19118*
77. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
78. [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*
79. [Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models](https://arxiv.org/abs/2310.06692) *— 2023 · arXiv:2310.06692*
80. [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*
81. [Intégration du raisonnement numérique dans les modèles de langue : État de l''art et direction de recherche](https://aclanthology.org/2023.jeptalnrecital-coria.11/) *— 2023 · Actes de CORIA-TALN 2023. Actes de la 18e Conférence en Recherche d'Information et Applications (CORIA)*
82. [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
83. [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*
84. [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*
85. [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) *— 2023 · arXiv:2303.17491*
86. [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*
87. [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*
88. [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*
89. [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://arxiv.org/abs/2309.05653) *— 2023 · arXiv:2309.05653*
90. [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)*
91. [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*
92. [Orca 2: Teaching Small Language Models How to Reason](https://arxiv.org/abs/2311.11045) *— 2023 · arXiv:2311.11045*
93. [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*
94. [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)*
95. [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://arxiv.org/abs/2304.09797) *— 2023 · arXiv:2304.09797*
96. [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://arxiv.org/abs/2309.16797) *— 2023 · arXiv:2309.16797*
97. [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*
98. [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*
99. [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*
100. [Through the Lens of Core Competency: Survey on Evaluation of Large Language Models](https://arxiv.org/abs/2308.07902) *— 2023 · arXiv:2308.07902*

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