---
title: "Who cites Solving General Arithmetic Word Problems — page 3"
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/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Solving General Arithmetic Word Problems](https://md.research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/) / cited by

# Who cites «Solving General Arithmetic Word Problems» · page 3

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

1. [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*
2. [GRACE: Discriminator-Guided Chain-of-Thought Reasoning](https://aclanthology.org/2023.findings-emnlp.1022/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
3. [Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models](https://arxiv.org/abs/2310.06692) *— 2023 · arXiv:2310.06692*
4. [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*
5. [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*
6. [Introduction to Mathematical Language Processing: Informal Proofs, Word Problems, and Supporting Tasks](https://aclanthology.org/2023.tacl-1.66/) *— 2023*
7. [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
8. [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*
9. [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) *— 2023 · arXiv:2303.17491*
10. [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*
11. [Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners](https://arxiv.org/abs/2305.14825) *— 2023 · arXiv:2305.14825*
12. [Large Language Models as Optimizers](https://arxiv.org/abs/2309.03409) *— 2023 · arXiv:2309.03409*
13. [Learning to Reason and Memorize with Self-Notes](https://arxiv.org/abs/2305.00833) *— 2023 · arXiv:2305.00833*
14. [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*
15. [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*
16. [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)*
17. [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://arxiv.org/abs/2309.05653) *— 2023 · arXiv:2309.05653*
18. [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)*
19. [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)*
20. [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*
21. [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*
22. [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)*
23. [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://arxiv.org/abs/2304.09797) *— 2023 · arXiv:2304.09797*
24. [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://arxiv.org/abs/2309.16797) *— 2023 · arXiv:2309.16797*
25. [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*
26. [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*
27. [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)*
28. [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)*
29. [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*
30. [Teaching Arithmetic to Small Transformers](https://arxiv.org/abs/2307.03381) *— 2023 · arXiv:2307.03381*
31. [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*
32. [Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration](https://arxiv.org/abs/2311.08152) *— 2023 · arXiv:2311.08152*
33. [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*
34. [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)*
35. [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://arxiv.org/abs/2308.09583) *— 2023 · arXiv:2308.09583*
36. [World Models for Math Story Problems](https://aclanthology.org/2023.findings-acl.579/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
37. [AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model](https://arxiv.org/abs/2208.01448) *— 2022 · arXiv:2208.01448*
38. [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*
39. [Automatic Chain of Thought Prompting in Large Language Models](https://arxiv.org/abs/2210.03493) *— 2022 · arXiv:2210.03493*
40. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) *— 2022 · arXiv:2201.11903*
41. [Complexity-Based Prompting for Multi-Step Reasoning](https://arxiv.org/abs/2210.00720) *— 2022 · arXiv:2210.00720*
42. [EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers](https://aclanthology.org/2022.acl-long.305/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [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*
44. [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)*
45. [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*
46. [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) *— 2022 · arXiv:2211.01910*
47. [Large language models are zero-shot reasoners](https://arxiv.org/abs/2205.11916) *— 2022*
48. [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://md.research.elamur.ai/free/en/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)*
49. [Mind”s Eye: Grounded Language Model Reasoning through Simulation](https://arxiv.org/abs/2210.05359) *— 2022 · arXiv:2210.05359*
50. [Noun-MWP: Math Word Problems Meet Noun Answers](https://aclanthology.org/2022.coling-1.338/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
51. [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)*
52. [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://arxiv.org/abs/2211.12588) *— 2022 · arXiv:2211.12588*
53. [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) *— 2022 · arXiv:2212.09597*
54. [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) *— 2022 · arXiv:2203.11171*
55. [Textual Enhanced Contrastive Learning for Solving Math Word Problems](https://aclanthology.org/2022.findings-emnlp.316/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
56. [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*
57. [A Bottom-Up DAG Structure Extraction Model for Math Word Problems](https://ojs.aaai.org/index.php/AAAI/article/view/16075) *— 2021 · AAAI 2021*
58. [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*
59. [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*
60. [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*
61. [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*
62. [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)*
63. [NaturalProofs: Mathematical Theorem Proving in Natural Language](https://arxiv.org/abs/2104.01112) *— 2021 · arXiv:2104.01112*
64. [Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks](https://aclanthology.org/2021.acl-long.456/) *— 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)*
65. [Representing Numbers in NLP: a Survey and a Vision](https://aclanthology.org/2021.naacl-main.53/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
66. [Training Verifiers to Solve Math Word Problems](https://arxiv.org/abs/2110.14168) *— 2021 · arXiv:2110.14168*
67. [A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers](https://aclanthology.org/2020.acl-main.92/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
68. [A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving](https://aclanthology.org/2020.emnlp-main.579/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
69. [An Empirical Investigation of Contextualized Number Prediction](https://aclanthology.org/2020.emnlp-main.385/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
70. [Graph-to-Tree Learning for Solving Math Word Problems](https://aclanthology.org/2020.acl-main.362/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
71. [Injecting Numerical Reasoning Skills into Language Models](https://aclanthology.org/2020.acl-main.89/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
72. [Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model](https://aclanthology.org/2020.emnlp-main.308/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
73. [Recurrent Inference in Text Editing](https://aclanthology.org/2020.findings-emnlp.159/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
74. [Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts](https://aclanthology.org/2020.icon-main.10/) *— 2020 · Proceedings of the 17th International Conference on Natural Language Processing (ICON)*
75. [Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension](https://aclanthology.org/D19-1609/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
76. [Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions](https://aclanthology.org/P19-1619/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
77. [NumNet: Machine Reading Comprehension with Numerical Reasoning](https://aclanthology.org/D19-1251/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
78. [ProblemSolver at SemEval-2019 Task 10: Sequence-to-Sequence Learning and Expression Trees](https://aclanthology.org/S19-2227/) *— 2019 · Proceedings of the 13th International Workshop on Semantic Evaluation*
79. [Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems](https://aclanthology.org/P19-1517/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
80. [SemEval-2019 Task 10: Math Question Answering](https://aclanthology.org/S19-2153/) *— 2019 · Proceedings of the 13th International Workshop on Semantic Evaluation*
81. [Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems](https://aclanthology.org/N19-1272/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
82. [Template-Based Math Word Problem Solvers with Recursive Neural Networks](https://ojs.aaai.org/index.php/AAAI/article/view/4697) *— 2019 · AAAI 2019*
83. [Text2Math: End-to-end Parsing Text into Math Expressions](https://aclanthology.org/D19-1536/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
84. [The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers](https://arxiv.org/abs/1808.07290) *— 2019 · IEEE TPAMI 42(9), 2020 (arXiv 1808.07290)*
85. [Tree-structured Decoding for Solving Math Word Problems](https://aclanthology.org/D19-1241/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
86. [Where's My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution](https://aclanthology.org/Q19-1030/) *— 2019 · Transactions of the Association for Computational Linguistics*
87. [A Meaning-Based Statistical English Math Word Problem Solver](https://aclanthology.org/N18-1060/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
88. [Data-Driven Methods for Solving Algebra Word Problems](https://arxiv.org/abs/1804.10718) *— 2018 · arXiv 1804.10718*
89. [DeepProbLog: Neural Probabilistic Logic Programming](https://arxiv.org/abs/1805.10872) *— 2018 · arXiv:1805.10872*
90. [EquGener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations](https://aclanthology.org/Y18-1053/) *— 2018 · Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation*
91. [Mapping to Declarative Knowledge for Word Problem Solving](https://md.research.elamur.ai/free/en/papers/roy-2018-mapping-declarative-knowledge-word-problem/) *— our card · 2018*
92. [MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning](https://ojs.aaai.org/index.php/AAAI/article/view/11981) *— 2018 · AAAI 2018*
93. [Neural Math Word Problem Solver with Reinforcement Learning](https://aclanthology.org/C18-1018/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
94. [Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers](https://aclanthology.org/P18-1196/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Translating a Math Word Problem to a Expression Tree](https://aclanthology.org/D18-1132/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
96. [Translating a Math Word Problem to an Expression Tree](https://arxiv.org/abs/1811.05632) *— 2018 · arXiv:1811.05632*
97. [Using Intermediate Representations to Solve Math Word Problems](https://aclanthology.org/P18-1039/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
98. [Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems](https://aclanthology.org/E17-1047/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
99. [Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers](https://aclanthology.org/D17-1083/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
100. [Deep Neural Solver for Math Word Problems](https://aclanthology.org/D17-1088/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*

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