---
title: "Who cites Solving General Arithmetic Word Problems — page 2"
description: "The papers of the Elamur Research library that cite «Solving General Arithmetic Word Problems», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/cited-by/page/2/"
---
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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.research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/)

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

1. [Adversarial Math Word Problem Generation](https://aclanthology.org/2024.findings-emnlp.292/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
2. [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)*
3. [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)*
4. [Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information](https://aclanthology.org/2024.lrec-main.81/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
5. [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)*
6. [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)*
7. [AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting](https://aclanthology.org/2024.lrec-main.160/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
8. [Better Zero-Shot Reasoning with Role-Play Prompting](https://aclanthology.org/2024.naacl-long.228/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
9. [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)*
10. [Can Language Models Learn Embeddings of Propositional Logic Assertions?](https://aclanthology.org/2024.lrec-main.246/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
11. [Chain-of-Thought Reasoning Without Prompting](https://arxiv.org/abs/2402.10200) *— 2024 · arXiv:2402.10200*
12. [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)*
13. [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*
14. [Cutting Through the Noise: Boosting LLM Performance on Math Word Problems](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/) *— our card · 2024 · arXiv:2406.15444*
15. [DDPrompt: Differential Diversity Prompting in Large Language Models](https://aclanthology.org/2024.acl-short.17/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
16. [Don”t Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization](https://arxiv.org/abs/2403.18120) *— 2024 · arXiv:2403.18120*
17. [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*
18. [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)*
19. [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*
20. [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://arxiv.org/abs/2401.10480) *— 2024 · arXiv:2401.10480*
21. [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*
22. [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*
23. [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*
24. [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)*
25. [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*
26. [From Bottom to Top: Extending the Potential of Parameter Efficient Fine-Tuning](https://aclanthology.org/2024.emnlp-main.204/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
27. [GLaPE: Gold Label-agnostic Prompt Evaluation for Large Language Models](https://aclanthology.org/2024.emnlp-main.121/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
28. [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)*
29. [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)*
30. [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*
31. [LLM can Achieve Self-Regulation via Hyperparameter Aware Generation](https://aclanthology.org/2024.findings-acl.396/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
32. [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*
33. [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)*
34. [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)*
35. [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*
36. [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)*
37. [MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark](https://aclanthology.org/2024.findings-acl.411/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
38. [MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://arxiv.org/abs/2403.14624) *— 2024 · arXiv:2403.14624*
39. [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)*
40. [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*
41. [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://arxiv.org/abs/2405.12205) *— 2024 · arXiv:2405.12205*
42. [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)*
43. [Multi-Step Reasoning with Large Language Models, a Survey](https://arxiv.org/abs/2407.11511) *— 2024 · arXiv:2407.11511*
44. [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195) *— 2024 · arXiv:2408.06195*
45. [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*
46. [Nash CoT: Multi-Path Inference with Preference Equilibrium](https://aclanthology.org/2024.emnlp-main.807/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
47. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://aclanthology.org/2024.acl-long.65/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Not All Votes Count! Programs as Verifiers Improve Self-Consistency of Language Models for Math Reasoning](https://arxiv.org/abs/2410.12608) *— 2024 · arXiv:2410.12608*
49. [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/abs/2402.14830) *— 2024 · arXiv:2402.14830*
50. [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*
51. [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)*
52. [Personality Alignment of Large Language Models](https://arxiv.org/abs/2408.11779) *— 2024 · arXiv:2408.11779*
53. [Prompt Engineering 101 Prompt Engineering Guidelines from a Linguistic Perspective](https://aclanthology.org/2024.ccl-1.108/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
54. [Prompt Engineering a Prompt Engineer](https://aclanthology.org/2024.findings-acl.21/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
55. [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)*
56. [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*
57. [ReFT: Representation Finetuning for Language Models](https://arxiv.org/abs/2404.03592) *— 2024 · arXiv:2404.03592*
58. [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)*
59. [S3Prompt: Instructing the Model with Self-calibration, Self-recall and Self-aggregation to Improve In-context Learning](https://aclanthology.org/2024.lrec-main.1242/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
60. [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*
61. [SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://arxiv.org/abs/2401.07950) *— 2024 · arXiv:2401.07950*
62. [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*
63. [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)*
64. [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*
65. [Sparse Matrix in Large Language Model Fine-tuning](https://arxiv.org/abs/2405.15525) *— 2024 · arXiv:2405.15525*
66. [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)*
67. [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*
68. [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)*
69. [Towards Human-aligned Evaluation for Linear Programming Word Problems](https://aclanthology.org/2024.lrec-main.1438/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
70. [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://arxiv.org/abs/2402.01622) *— 2024 · arXiv:2402.01622*
71. [Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework](https://aclanthology.org/2024.lrec-main.1465/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
72. [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*
73. [Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://arxiv.org/abs/2402.05376) *— 2024 · arXiv:2402.05376*
74. [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)*
75. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
76. [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)*
77. [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)*
78. [ARB: Advanced Reasoning Benchmark for Large Language Models](https://arxiv.org/abs/2307.13692) *— 2023 · arXiv:2307.13692*
79. [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://arxiv.org/abs/2310.03710) *— 2023 · arXiv:2310.03710*
80. [Analyzing ChatGPT”s Mathematical Deficiencies: Insights and Contributions](https://aclanthology.org/2023.rocling-1.22/) *— 2023 · Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)*
81. [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*
82. [Auto-Regressive Next-Token Predictors are Universal Learners](https://arxiv.org/abs/2309.06979) *— 2023 · arXiv:2309.06979*
83. [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*
84. [Better Zero-Shot Reasoning with Role-Play Prompting](https://arxiv.org/abs/2308.07702) *— 2023 · arXiv:2308.07702*
85. [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*
86. [Chain of Thought Prompting Elicits Knowledge Augmentation](https://aclanthology.org/2023.findings-acl.408/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
87. [ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision](https://aclanthology.org/2023.eacl-main.186/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
88. [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://arxiv.org/abs/2310.00280) *— 2023 · arXiv:2310.00280*
89. [DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning](https://arxiv.org/abs/2310.02954) *— 2023 · arXiv:2310.02954*
90. [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) *— 2023 · ICLR 2023 (arXiv 2210.02406)*
91. [Deductive Verification of Chain-of-Thought Reasoning](https://arxiv.org/abs/2306.03872) *— 2023 · arXiv:2306.03872*
92. [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*
93. [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*
94. [DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks](https://arxiv.org/abs/2309.17167) *— 2023 · arXiv:2309.17167*
95. [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*
96. [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://arxiv.org/abs/2305.19118) *— 2023 · arXiv:2305.19118*
97. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
98. [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*
99. [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)*
100. [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)*

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