[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-5/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 5

# Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 5

592 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/patel-2021-are-nlp-models-really-able/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-4/) · **5** · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-6/)

1. [Large language models are zero-shot reasoners](https://arxiv.org/abs/2205.11916) *— 2022*
2. [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)*
3. [MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers](https://arxiv.org/abs/2109.00799) *— 2022 · AAAI 2022 demo (arXiv 2109.00799)*
4. [Mind''s Eye: Grounded Language Model Reasoning through Simulation](https://arxiv.org/abs/2210.05359) *— 2022 · arXiv:2210.05359*
5. [Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem](https://aclanthology.org/2022.findings-emnlp.79/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
6. [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) *— 2022 · arXiv:2211.10435*
7. [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/abs/2204.02311) *— 2022 · arXiv:2204.02311*
8. [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://arxiv.org/abs/2206.10498) *— 2022 · arXiv:2206.10498*
9. [Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers](https://aclanthology.org/2022.naacl-main.310/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
10. [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://arxiv.org/abs/2211.12588) *— 2022 · arXiv:2211.12588*
11. [Reasoning Like Program Executors](https://aclanthology.org/2022.emnlp-main.48/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
12. [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) *— 2022 · arXiv:2212.09597*
13. [Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems](https://aclanthology.org/2022.findings-acl.195/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
14. [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) *— 2022 · arXiv:2203.11171*
15. [Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems](https://aclanthology.org/2022.emnlp-main.542/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
16. [UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131) *— 2022 · arXiv:2205.05131*
17. [Unbiased Math Word Problems Benchmark for Mitigating Solving Bias](https://aclanthology.org/2022.findings-naacl.104/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
18. [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*
19. [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://aclanthology.org/2021.findings-emnlp.350/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
20. [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*
21. [Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems](https://arxiv.org/abs/2111.05364) *— 2021 · arXiv 2111.05364*
22. abdaljalil-2025-audit-understanding-posterior-constrained-inference
23. agarwal-2025-step-step-unmasking-parameter-efficient
24. ahmed-2025-bennumeval-benchmark-assess-llms-numerical
25. alazraki-2026-agentcoma-compositional-benchmark-mixing-commonsense
26. an-2024-can-llms-learn-mistakes-empirical
27. baeumel-2025-disentangling-mathematical-reasoning-llms-methodological
28. ban-2026-rethinking-parameter-sharing-llm-fine
29. bao-2025-fixing-distribution-shifts-llm-self
30. belanec-2026-peft-bench-parameter-efficient-fine
31. belanec-2026-peft-factory-unified-parameter-efficient
32. boguraev-2024-models-should-embrace-communicative-nature
33. cang-2026-graph-grpo-stabilizing-multi-agent
34. cao-2026-comol-efficient-mixture-lora-experts
35. cao-2026-diffcot-diffusion-styled-chain-thought
36. cao-2026-fusion-training-mathematical-generalization-large
37. cao-2026-moa-heterogeneous-mixture-adapters-parameter
38. cecere-2025-monte-carlo-temperature-robust-sampling
39. cha-2022-noun-mwp-math-word-problems
40. chang-2026-unlocking-black-box-latent-reasoning
41. chen-2024-llm-reasoning-engine-specialized-training
42. chen-2024-s3prompt-instructing-model-self-calibration
43. chen-2024-self-para-consistency-improving-reasoning
44. chen-2025-guilomo-allocating-experts-ranks-lora
45. chen-2026-dynamic-pmi-guided-contrastive-decoding
46. chen-2026-how-do-answer-tokens-read
47. cheng-2023-analyzing-chatgpt-s-mathematical-deficiencies
48. christ-2026-edumath-generating-standards-aligned-educational
49. chu-2024-navigate-through-enigmatic-labyrinth-survey
50. chu-2025-faithful-multi-step-reasoning-through
51. chung-2025-revisiting-test-time-scaling-survey
52. criado-2026-benchmarking-mathematical-reasoning-low-resource
53. cui-2026-mind-passive-mimicry-active-reasoning
54. dentan-2026-much-multilingual-claim-hallucination-benchmark
55. ding-2026-empowering-multi-turn-tool-integrated
56. dutta-2023-frugal-lms-trained-invoke-symbolic
57. feng-2025-comoe-contrastive-representation-mixture-experts
58. ferguson-2025-evaluating-meta-object-level-reasoning
59. foster-2026-ai-research-preference-models
60. gangwar-2026-integrating-arithmetic-learning-improves-mathematical
61. gemechu-2025-natural-language-reasoning-large-language
62. gholami-2024-gold-generalized-knowledge-distillation-out
63. gong-2026-punctuation-steered-representation-fine-tuning
64. gou-2025-mitigating-demonstration-bias-through-global
65. griot-2026-compress-distill-reasoning-trace-compression
66. gu-2024-bottom-top-extending-potential-parameter
67. gu-2026-tera-vector-based-random-tensor
68. guo-2026-failures-are-treasures-constructing-pedagogical
69. gupta-2026-iris-interleaved-reinforcement-incremental-staged
70. ha-2025-dsg-mcts-dynamic-strategy-guided
71. ha-2026-aha-moments-controllable-thinking-meta
72. hammal-2026-kad-framework-proxy-based-test
73. hammal-2026-rejection-criterion-proxy-based-test
74. han-2025-cat-causal-attention-tuning-injecting
75. hao-2025-dynamic-collaboration-multi-language-models
76. hou-2026-pseudo-balancing-true-specialization-memory
77. hou-2026-smart-evaluating-llms-mathematical-reasoning
78. hu-2023-generation-based-deductive-method-math
79. hu-2026-rethinking-reasoning-survey-reasoning-based
80. huang-2025-enhancing-chain-thought-reasoning-critical
81. hwang-2025-can-you-trick-grader-adversarial
82. jain-2025-undo-understanding-distillation-optimization
83. jiang-2025-dart-distilling-autoregressive-reasoning-silent
84. jin-2023-human-loop-optimization-autoformalism-approach
85. jin-2025-parrot-training-pipeline-enhances-both
86. kadlcik-2024-self-training-language-models-arithmetic
87. kale-2026-future-confidence-distillation-large-language
88. kassem-2026-how-robust-are-router-llms
89. ki-2026-adamame-training-recipe-adaptive-multilingual
90. kim-2023-athena-mathematical-reasoning-thought-expansion
91. kim-2024-saas-solving-ability-amplification-strategy
92. kim-2025-ccqa-generating-question-solution-can
93. kong-2024-better-zero-shot-reasoning-role
94. kong-2026-inference-time-rethinking-latent-thought
95. kowsher-2025-predicting-through-generation-why-generation
96. leang-2026-picsar-probabilistic-confidence-selection-ranking
97. lee-2025-automatic-mathematic-context-example-generation
98. lee-2025-curriculum-debiasing-robust-parameter-efficient
99. lee-2025-gemmas-graph-based-evaluation-metrics
100. lee-2026-cirf-tokenizing-chain-thoughts-reusable

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-4/) · **5** · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-6/)

- [← previous Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-4/)
- [next → Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/patel-2021-are-nlp-models-really-able-6/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)
