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# Who cites «Solving General Arithmetic Word Problems»

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** · [2](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. [Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models](https://aclanthology.org/2026.findings-acl.821/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning](https://aclanthology.org/2026.acl-long.2175/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication](https://aclanthology.org/2026.findings-acl.1258/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
4. [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*
5. [GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering](https://aclanthology.org/2026.findings-acl.319/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [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*
7. [Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction](https://aclanthology.org/2026.findings-acl.1168/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning](https://aclanthology.org/2026.acl-long.2097/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [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)*
10. [TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.207/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
11. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
12. [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*
13. [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)*
14. [AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration](https://aclanthology.org/2025.acl-long.1170/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction](https://aclanthology.org/2025.emnlp-main.584/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
16. [ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving](https://arxiv.org/abs/2501.08203) *— 2025 · arXiv:2501.08203*
17. [Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models](https://aclanthology.org/2025.emnlp-main.567/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
18. [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)*
19. [CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation](https://aclanthology.org/2025.emnlp-main.36/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
20. [CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning](https://aclanthology.org/2025.emnlp-main.1024/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
21. [CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models](https://aclanthology.org/2025.findings-emnlp.168/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
22. [Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs](https://aclanthology.org/2025.coling-main.703/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
23. [DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning](https://aclanthology.org/2025.naacl-long.96/) *— 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)*
24. [DiPT: Enhancing LLM Reasoning through Diversified Perspective-Taking](https://aclanthology.org/2025.findings-naacl.356/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
25. [Do Large Language Model Benchmarks Test Reliability?](https://arxiv.org/abs/2502.03461) *— 2025 · arXiv:2502.03461*
26. [Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning](https://aclanthology.org/2025.coling-main.609/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
27. [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*
28. [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)*
29. [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*
30. [FLARE: Faithful Logic-Aided Reasoning and Exploration](https://aclanthology.org/2025.emnlp-main.1193/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
31. [First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning](https://aclanthology.org/2025.findings-acl.42/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
32. [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*
33. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
34. [Learning Text Styles: A Study on Transfer, Attribution, and Verification](https://arxiv.org/abs/2507.16530) *— 2025 · arXiv:2507.16530*
35. [LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs](https://aclanthology.org/2025.findings-acl.1247/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
36. [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*
37. [Nudging: Inference-time Alignment of LLMs via Guided Decoding](https://aclanthology.org/2025.acl-long.623/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [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*
39. [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*
40. [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*
41. [Propulsion: Steering LLM with Tiny Fine-Tuning](https://aclanthology.org/2025.coling-main.506/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
42. [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)*
43. [SIKeD: Self-guided Iterative Knowledge Distillation for Mathematical Reasoning](https://aclanthology.org/2025.findings-acl.513/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
44. [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)*
45. [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*
46. [Theorem Prover as a Judge for Synthetic Data Generation](https://aclanthology.org/2025.acl-long.1448/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains](https://arxiv.org/abs/2505.16552) *— 2025 · arXiv:2505.16552*
48. [Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning](https://aclanthology.org/2025.emnlp-main.321/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
49. [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*
50. [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://arxiv.org/abs/2501.09686) *— 2025 · arXiv:2501.09686*
51. [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*
52. [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)*
53. [Uncertainty-Aware Contrastive Decoding](https://aclanthology.org/2025.findings-acl.1352/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
54. [Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems](https://aclanthology.org/2025.emnlp-main.623/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
55. [When natural language is not enough: The limits of in-context learning demonstrations in multilingual reasoning](https://aclanthology.org/2025.findings-naacl.412/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
56. [``Well, Keep Thinking'''': Enhancing LLM Reasoning with Adaptive Injection Decoding](https://aclanthology.org/2025.findings-acl.520/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
57. [A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages](https://aclanthology.org/2024.findings-naacl.78/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
58. [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*
59. [Adversarial Math Word Problem Generation](https://aclanthology.org/2024.findings-emnlp.292/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
60. [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)*
61. [Aligning Large and Small Language Models via Chain-of-Thought Reasoning](https://aclanthology.org/2024.eacl-long.109/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Ask Again, Then Fail: Large Language Models'' Vacillations in Judgment](https://aclanthology.org/2024.acl-long.577/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study](https://aclanthology.org/2024.naacl-long.4/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
64. [Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://aclanthology.org/2024.acl-long.271/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [Chain-of-Thought Reasoning Without Prompting](https://arxiv.org/abs/2402.10200) *— 2024 · arXiv:2402.10200*
66. [ChatGPT Rates Natural Language Explanation Quality like Humans: But on Which Scales?](https://aclanthology.org/2024.lrec-main.277/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
67. [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*
68. [Cutting Through the Noise: Boosting LLM Performance on Math Word Problems](https://md.mova.elamur.ai/free/en/docs/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/) *— our card · 2024 · arXiv:2406.15444*
69. [Don''t Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization](https://arxiv.org/abs/2403.18120) *— 2024 · arXiv:2403.18120*
70. [EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning](https://aclanthology.org/2024.emnlp-main.307/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
71. [EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning](https://aclanthology.org/2024.naacl-short.35/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
72. [Empowering Multi-step Reasoning across Languages via Program-Aided Language Models](https://aclanthology.org/2024.emnlp-main.678/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
73. [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://arxiv.org/abs/2401.10480) *— 2024 · arXiv:2401.10480*
74. [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*
75. [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*
76. [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*
77. [FinanceMATH: Knowledge-Intensive Math Reasoning in Finance Domains](https://aclanthology.org/2024.acl-long.693/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
78. [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*
79. [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)*
80. [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)*
81. [Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios](https://aclanthology.org/2024.findings-acl.230/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
82. [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*
83. [Learning to Compress Prompt in Natural Language Formats](https://aclanthology.org/2024.naacl-long.429/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
84. [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)*
85. [LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models](https://aclanthology.org/2024.emnlp-main.128/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
86. [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)*
87. [MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://arxiv.org/abs/2403.14624) *— 2024 · arXiv:2403.14624*
88. [Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models](https://aclanthology.org/2024.lrec-main.980/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
89. [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*
90. [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://arxiv.org/abs/2405.12205) *— 2024 · arXiv:2405.12205*
91. [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)*
92. [Multi-Step Reasoning with Large Language Models, a Survey](https://arxiv.org/abs/2407.11511) *— 2024 · arXiv:2407.11511*
93. [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195) *— 2024 · arXiv:2408.06195*
94. [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*
95. [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/abs/2402.14830) *— 2024 · arXiv:2402.14830*
96. [PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling](https://aclanthology.org/2024.emnlp-main.226/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
97. [PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning](https://aclanthology.org/2024.naacl-long.142/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
98. [Personality Alignment of Large Language Models](https://arxiv.org/abs/2408.11779) *— 2024 · arXiv:2408.11779*
99. [Prompt Engineering a Prompt Engineer](https://aclanthology.org/2024.findings-acl.21/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
100. [QDMR-based Planning-and-Solving Prompting for Complex Reasoning Tasks](https://aclanthology.org/2024.lrec-main.1173/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*

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