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

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/)

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1. [Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards](https://aclanthology.org/2026.acl-long.1978/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [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*
3. [Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models](https://aclanthology.org/2026.acl-long.135/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models](https://aclanthology.org/2026.acl-long.315/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [C$^3$D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding](https://aclanthology.org/2026.findings-acl.33/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [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)*
7. [CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems](https://aclanthology.org/2026.acl-long.815/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs](https://aclanthology.org/2026.acl-long.660/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration](https://aclanthology.org/2026.acl-long.298/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving](https://aclanthology.org/2026.findings-acl.1810/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
11. [Evidential Semantic Entropy for LLM Uncertainty Quantification](https://aclanthology.org/2026.eacl-long.334/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [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*
13. [FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner](https://aclanthology.org/2026.acl-long.1790/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation](https://aclanthology.org/2026.findings-eacl.227/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
15. [Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation](https://aclanthology.org/2026.acl-long.666/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [Finding the Cracks: Improving LLMs Reasoning with Paraphrastic Probing and Consistency Verification](https://arxiv.org/abs/2602.11361) *— 2026 · arXiv:2602.11361*
17. [FlowHN: Adaptive Token Routing for Efficient Parallel Hybrid Networks](https://aclanthology.org/2026.acl-industry.83/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)*
18. [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*
19. [From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs](https://aclanthology.org/2026.findings-acl.1420/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
20. [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*
21. [GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection](https://aclanthology.org/2026.eacl-long.206/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [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*
23. [GSM-Noise: Exploring and Enhancing Large Language Models'' Reasoning under Noisy Inputs](https://aclanthology.org/2026.findings-acl.1748/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
24. [HIPO: A Hierarchical Prompt Optimization Framework with Task Awareness and Fine-Grained Debugging](https://aclanthology.org/2026.findings-acl.996/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
25. [How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation](https://aclanthology.org/2026.findings-acl.255/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
26. [LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?](https://aclanthology.org/2026.acl-long.122/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [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)*
28. [MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support](https://aclanthology.org/2026.eacl-long.132/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [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*
30. [Multi-LLM Collaborative Search for Complex Problem Solving](https://aclanthology.org/2026.findings-acl.2115/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
31. [PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding](https://aclanthology.org/2026.findings-acl.1234/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
32. [Policy-Guided Stepwise Action Planning for Controllable LLM Reasoning](https://aclanthology.org/2026.findings-acl.2024/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
33. [PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models](https://aclanthology.org/2026.findings-acl.1869/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
34. [Reflect, Rewrite, Repeat: How Simple Arithmetic Enables Advanced Reasoning in Small Language Models](https://aclanthology.org/2026.findings-eacl.69/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
35. [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)*
36. [Self-Consistency from Only Two Samples: CoT–PoT Ensembling for Efficient LLM Reasoning](https://aclanthology.org/2026.findings-acl.1642/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
37. [Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning](https://aclanthology.org/2026.findings-acl.1915/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
38. [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)*
39. [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*
40. [Taming ``Zombie'''' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution](https://aclanthology.org/2026.acl-long.373/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignment](https://aclanthology.org/2026.acl-srw.74/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
42. [Unlocking Human-Like Visible Logic: How Logic Diagrams Boost Logic Reasoning in Large Language Models?](https://aclanthology.org/2026.findings-acl.1602/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
43. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
44. [A Survey on Large Language Models for Mathematical Reasoning](https://arxiv.org/abs/2506.08446) *— 2025 · arXiv:2506.08446*
45. [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*
46. [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)*
47. [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)*
48. [An Analysis of the Impact of Problem Paraphrasing on LLM-Based Mathematical Problem Solving](https://aclanthology.org/2025.ijcnlp-long.23/) *— 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*
49. [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*
50. [Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks](https://aclanthology.org/2025.acl-long.317/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
51. [Auto-Discovery-Bench: Diagnosing Structured State Tracking in Oracle-Guided Discovery](https://arxiv.org/abs/2502.15224) *— 2025 · arXiv:2502.15224*
52. [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*
53. [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)*
54. [Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity](https://aclanthology.org/2025.findings-acl.234/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
55. [Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems](https://aclanthology.org/2025.findings-emnlp.1192/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
56. [CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning](https://aclanthology.org/2025.emnlp-main.302/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
57. [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*
58. [Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?](https://aclanthology.org/2025.acl-long.321/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation](https://aclanthology.org/2025.emnlp-main.723/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
60. [CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought](https://aclanthology.org/2025.findings-acl.1339/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
61. [Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs](https://aclanthology.org/2025.emnlp-main.130/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
62. [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*
63. [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)*
64. [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*
65. [Do Large Language Model Benchmarks Test Reliability?](https://arxiv.org/abs/2502.03461) *— 2025 · arXiv:2502.03461*
66. [Efficient Tool Use with Chain-of-Abstraction Reasoning](https://aclanthology.org/2025.coling-main.185/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
67. [Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models](https://arxiv.org/abs/2501.02599) *— 2025 · arXiv:2501.02599*
68. [EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models](https://aclanthology.org/2025.findings-emnlp.913/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
69. [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*
70. [Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning](https://aclanthology.org/2025.naacl-long.515/) *— 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)*
71. [Entropy-based Exploration Conduction for Multi-step Reasoning](https://aclanthology.org/2025.findings-acl.201/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
72. [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*
73. [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)*
74. [Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions](https://aclanthology.org/2025.findings-acl.1172/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
75. [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*
76. [Exposing the Achilles'' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning](https://aclanthology.org/2025.acl-long.1313/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [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*
78. [Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs](https://aclanthology.org/2025.acl-long.191/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [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*
80. [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*
81. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
82. [Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions](https://aclanthology.org/2025.acl-long.843/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
83. [Improving LLM Reasoning through Interpretable Role-Playing Steering](https://aclanthology.org/2025.findings-emnlp.39/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
84. [Improving LLMs' Generalized Reasoning Abilities by Graph Problems](https://arxiv.org/abs/2507.17168) *— 2025 · arXiv:2507.17168*
85. [Improving Reasoning Capabilities in Small Models through Mixture-of-layers Distillation with Stepwise Attention on Key Information](https://aclanthology.org/2025.emnlp-main.250/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
86. [Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models](https://aclanthology.org/2025.acl-long.635/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloud](https://arxiv.org/abs/2508.06948) *— 2025 · arXiv:2508.06948*
88. [LEMMA: Learning from Errors for MatheMatical Advancement in LLMs](https://aclanthology.org/2025.findings-acl.605/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
89. [LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark for Comprehensive Evaluation of LLMs](https://aclanthology.org/2025.naacl-long.161/) *— 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)*
90. [Learning Text Styles: A Study on Transfer, Attribution, and Verification](https://arxiv.org/abs/2507.16530) *— 2025 · arXiv:2507.16530*
91. [Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review](https://aclanthology.org/2025.findings-acl.217/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
92. [Let''s Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models](https://aclanthology.org/2025.findings-acl.795/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
93. [MATH-Perturb: Benchmarking LLMs'' Math Reasoning Abilities against Hard Perturbations](https://arxiv.org/abs/2502.06453) *— 2025 · arXiv:2502.06453*
94. [MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning](https://aclanthology.org/2025.emnlp-main.1660/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
95. [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*
96. [Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges](https://arxiv.org/abs/2502.08680) *— 2025 · arXiv:2502.08680*
97. [MegaMath: Pushing the Limits of Open Math Corpora](https://arxiv.org/abs/2504.02807) *— 2025 · arXiv:2504.02807*
98. [Meta-Reasoning Improves Tool Use in Large Language Models](https://aclanthology.org/2025.findings-naacl.440/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
99. [Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling](https://aclanthology.org/2025.naacl-long.533/) *— 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)*
100. [MultiNRC: A Challenging and Native Multilingual Reasoning Evaluation Benchmark for LLMs](https://arxiv.org/abs/2507.17476) *— 2025 · arXiv:2507.17476*

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