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# Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 2

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. [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)*
2. [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*
3. [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*
4. [PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems](https://aclanthology.org/2025.findings-ijcnlp.43/) *— 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*
5. [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*
6. [Probing for Arithmetic Errors in Language Models](https://aclanthology.org/2025.emnlp-main.411/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
7. [Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning](https://aclanthology.org/2025.findings-acl.440/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
8. [PromptWizard: Optimizing Prompts via Task-Aware, Feedback-Driven Self-Evolution](https://aclanthology.org/2025.findings-acl.1025/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
9. [Propulsion: Steering LLM with Tiny Fine-Tuning](https://aclanthology.org/2025.coling-main.506/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
10. [QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models](https://aclanthology.org/2025.emnlp-main.724/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
11. [Reasoning with Latent Thoughts: On the Power of Looped Transformers](https://arxiv.org/abs/2502.17416) *— 2025 · arXiv:2502.17416*
12. [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)*
13. [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*
14. [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)*
15. [Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models](https://aclanthology.org/2025.acl-long.29/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching](https://aclanthology.org/2025.emnlp-main.1236/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
17. [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*
18. [Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework](https://aclanthology.org/2025.emnlp-main.1541/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
19. [Superficial Self-Improved Reasoners Benefit from Model Merging](https://aclanthology.org/2025.emnlp-main.301/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
20. [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)*
21. [Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains](https://arxiv.org/abs/2505.16552) *— 2025 · arXiv:2505.16552*
22. [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*
23. [Token-Efficient RL for LLM Reasoning](https://arxiv.org/abs/2504.20834) *— 2025 · arXiv:2504.20834*
24. [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*
25. [Towards Robust Mathematical Reasoning](https://aclanthology.org/2025.emnlp-main.1794/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
26. [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*
27. [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)*
28. [Uncertainty-Aware Contrastive Decoding](https://aclanthology.org/2025.findings-acl.1352/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
29. [Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap](https://aclanthology.org/2025.mrl-main.6/) *— 2025 · Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)*
30. [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*
31. [Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning](https://aclanthology.org/2025.findings-acl.782/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
32. [VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning](https://aclanthology.org/2025.emnlp-main.642/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
33. [Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training](https://aclanthology.org/2025.acl-long.813/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors](https://arxiv.org/abs/2504.04785) *— 2025 · arXiv:2504.04785*
35. [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*
36. [Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels](https://aclanthology.org/2025.coling-main.251/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
37. [基于思维链和知识迁移的多语言问答推理研究](https://aclanthology.org/2025.ccl-1.14/) *— 2025 · Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)*
38. [3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability](https://arxiv.org/abs/2409.00119) *— 2024 · arXiv:2409.00119*
39. [A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers](https://aclanthology.org/2024.naacl-long.84/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
40. [API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs](https://aclanthology.org/2024.acl-long.694/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [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*
42. [Advancing LLM Reasoning Generalists with Preference Trees](https://arxiv.org/abs/2404.02078) *— 2024 · arXiv:2404.02078*
43. [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)*
44. [Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models](https://arxiv.org/abs/2406.02061) *— 2024 · arXiv:2406.02061*
45. [AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality](https://aclanthology.org/2024.emnlp-main.1141/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
46. [An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning](https://arxiv.org/abs/2403.00799) *— 2024 · arXiv:2403.00799*
47. [ApiQ: Finetuning of 2-Bit Quantized Large Language Model](https://aclanthology.org/2024.emnlp-main.1168/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
48. [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)*
49. [Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435) *— 2024 · arXiv:2408.08435*
50. [Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem](https://aclanthology.org/2024.lrec-main.196/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
51. [Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws](https://arxiv.org/abs/2401.00448) *— 2024 · arXiv:2401.00448*
52. [Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models](https://aclanthology.org/2024.findings-emnlp.360/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
53. [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)*
54. [Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations](https://aclanthology.org/2024.findings-emnlp.411/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
55. [COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training](https://arxiv.org/abs/2410.19313) *— 2024 · arXiv:2410.19313*
56. [Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages](https://aclanthology.org/2024.paclic-1.61/) *— 2024 · Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation*
57. [Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs](https://aclanthology.org/2024.emnlp-main.629/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
58. [Common 7B Language Models Already Possess Strong Math Capabilities](https://arxiv.org/abs/2403.04706) *— 2024 · arXiv:2403.04706*
59. [Competition-Level Problems are Effective LLM Evaluators](https://aclanthology.org/2024.findings-acl.803/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
60. [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*
61. [ControlMath: Controllable Data Generation Promotes Math Generalist Models](https://aclanthology.org/2024.emnlp-main.680/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
62. [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*
63. [DataComp-LM: In search of the next generation of training sets for language models](https://arxiv.org/abs/2406.11794) *— 2024 · arXiv:2406.11794*
64. [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://arxiv.org/abs/2401.14196) *— 2024 · arXiv:2401.14196*
65. [Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models](https://arxiv.org/abs/2411.01141) *— 2024 · arXiv:2411.01141*
66. [DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents](https://aclanthology.org/2024.acl-long.852/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
67. [DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models](https://aclanthology.org/2024.emnlp-main.814/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
68. [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)*
69. [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*
70. [Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models](https://aclanthology.org/2024.findings-naacl.257/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
71. [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://arxiv.org/abs/2401.10480) *— 2024 · arXiv:2401.10480*
72. [Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering](https://aclanthology.org/2024.findings-acl.231/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
73. [Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction](https://aclanthology.org/2024.findings-acl.673/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
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. [Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems](https://aclanthology.org/2024.emnlp-main.915/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
77. [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*
78. [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)*
79. [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*
80. [From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting](https://aclanthology.org/2024.nlrse-1.7/) *— 2024 · Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)*
81. [GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution](https://aclanthology.org/2024.eacl-long.7/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
82. [GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements](https://arxiv.org/abs/2402.10963) *— 2024 · arXiv:2402.10963*
83. [GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers](https://aclanthology.org/2024.acl-long.163/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
84. [Give me a hint: Can LLMs take a hint to solve math problems?](https://arxiv.org/abs/2410.05915) *— 2024 · arXiv:2410.05915*
85. [Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing](https://aclanthology.org/2024.insights-1.15/) *— 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP*
86. [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://aclanthology.org/2024.lrec-main.708/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
87. [How Do Humans Write Code? Large Models Do It the Same Way Too](https://aclanthology.org/2024.emnlp-main.267/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
88. [How to Train Data-Efficient LLMs](https://arxiv.org/abs/2402.09668) *— 2024 · arXiv:2402.09668*
89. [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)*
90. [Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint](https://aclanthology.org/2024.findings-acl.338/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
91. [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)*
92. [Interpreting and Improving Large Language Models in Arithmetic Calculation](https://arxiv.org/abs/2409.01659) *— 2024 · arXiv:2409.01659*
93. [Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning](https://aclanthology.org/2024.emnlp-main.1004/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
94. [JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models](https://arxiv.org/abs/2405.14365) *— 2024 · arXiv:2405.14365*
95. [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*
96. [Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning](https://arxiv.org/abs/2403.02333) *— 2024 · arXiv:2403.02333*
97. [Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method](https://aclanthology.org/2024.naacl-long.390/) *— 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. [L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://aclanthology.org/2024.tacl-1.72/) *— 2024 · Transactions of the Association for Computational Linguistics*
99. [LangBridge: Multilingual Reasoning Without Multilingual Supervision](https://aclanthology.org/2024.acl-long.405/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
100. [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*

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