---
title: "Who cites Learning to Solve Arithmetic Word Problems with…"
description: "The papers of the Elamur Research library that cite «Learning to Solve Arithmetic Word Problems with Verb Categorization», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/"
---
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# Who cites «Learning to Solve Arithmetic Word Problems with Verb Categorization»

222 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/hosseini-2014-learning-solve-arithmetic-word-problems/)

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/page/3/)

1. [CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging](https://aclanthology.org/2026.findings-acl.811/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [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*
3. [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*
4. [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*
5. [Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models](https://aclanthology.org/2026.lrec-1.398/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
6. [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)*
7. [Math-DB: A Discourse Framework for Mathematical Word Problems to Enhance LLM Reasoning](https://aclanthology.org/2026.law-main.7/) *— 2026 · Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)*
8. [MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models](https://aclanthology.org/2026.acl-long.965/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [On LLM Prompting Techniques for Arabic Language Arithmetic Reasoning](https://aclanthology.org/2026.osact-1.13/) *— 2026 · The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks*
10. [Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules](https://aclanthology.org/2026.findings-eacl.232/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
11. [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)*
12. [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*
13. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
14. [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*
15. [AdaDHP: Fine-Grained Fine-Tuning via Dual Hadamard Product and Adaptive Parameter Selection](https://aclanthology.org/2025.acl-long.467/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [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)*
17. [AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation](https://arxiv.org/abs/2510.19361) *— 2025 · arXiv:2510.19361*
18. [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)*
19. [BenNumEval: A Benchmark to Assess LLMs” Numerical Reasoning Capabilities in Bengali](https://aclanthology.org/2025.findings-acl.915/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
20. [Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective](https://aclanthology.org/2025.acl-long.1213/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning](https://aclanthology.org/2025.findings-emnlp.398/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
22. [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)*
23. [Disentangling Mathematical Reasoning in LLMs: A Methodological Investigation of Internal Mechanisms](https://aclanthology.org/2025.mathnlp-main.16/) *— 2025 · Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)*
24. [Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units](https://aclanthology.org/2025.emnlp-main.651/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
25. [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*
26. [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)*
27. [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*
28. [Exploring Backward Reasoning in Large Language Models](https://aclanthology.org/2025.findings-naacl.366/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
29. [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*
30. [Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models](https://aclanthology.org/2025.findings-acl.586/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
31. [Glider: Global and Local Instruction-Driven Expert Router](https://aclanthology.org/2025.emnlp-main.319/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
32. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
33. [Learning Text Styles: A Study on Transfer, Attribution, and Verification](https://arxiv.org/abs/2507.16530) *— 2025 · arXiv:2507.16530*
34. [LoRACoE: Improving Large Language Model via Composition-based LoRA Expert](https://aclanthology.org/2025.emnlp-main.1594/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
35. [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*
36. [Mitigating Demonstration Bias through Global Coevolutionary Reasoning](https://aclanthology.org/2025.findings-acl.450/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
37. [NeuroAda: Activating Each Neuron”s Potential for Parameter-Efficient Fine-Tuning](https://aclanthology.org/2025.emnlp-main.555/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
38. [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*
39. [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*
40. [Predicting Through Generation: Why Generation Is Better for Prediction](https://aclanthology.org/2025.acl-long.1303/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [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*
42. [Propulsion: Steering LLM with Tiny Fine-Tuning](https://aclanthology.org/2025.coling-main.506/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
43. [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)*
44. [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)*
45. [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*
46. [Step-by-Step Unmasking for Parameter-Efficient Fine-Tuning of Large Language Models](https://aclanthology.org/2025.tacl-1.79/) *— 2025 · Transactions of the Association for Computational Linguistics*
47. [Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models](https://aclanthology.org/2025.coling-industry.69/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics: Industry Track*
48. [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*
49. [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://arxiv.org/abs/2501.09686) *— 2025 · arXiv:2501.09686*
50. [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*
51. [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)*
52. [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*
53. [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*
54. [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)*
55. [AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations](https://aclanthology.org/2024.findings-emnlp.163/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
56. [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)*
57. [Bridging the Gap between Different Vocabularies for LLM Ensemble](https://aclanthology.org/2024.naacl-long.395/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
58. [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*
59. [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)*
60. [Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving](https://arxiv.org/abs/2404.03938) *— 2024 · arXiv:2404.03938 · SN Computer Science, 5, 506 (2024)*
61. [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)*
62. [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*
63. [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://arxiv.org/abs/2401.10480) *— 2024 · arXiv:2401.10480*
64. [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*
65. [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*
66. [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*
67. [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*
68. [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*
69. [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*
70. [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)*
71. [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)*
72. [Interpreting and Improving Large Language Models in Arithmetic Calculation](https://arxiv.org/abs/2409.01659) *— 2024 · arXiv:2409.01659*
73. [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*
74. [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)*
75. [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)*
76. [Making Large Language Models Better Reasoners with Orchestrated Streaming Experiences](https://aclanthology.org/2024.emnlp-main.48/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
77. [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*
78. [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*
79. [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://arxiv.org/abs/2405.12205) *— 2024 · arXiv:2405.12205*
80. [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)*
81. [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*
82. [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*
83. [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)*
84. [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*
85. [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/abs/2402.14830) *— 2024 · arXiv:2402.14830*
86. [Personality Alignment of Large Language Models](https://arxiv.org/abs/2408.11779) *— 2024 · arXiv:2408.11779*
87. [Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models](https://aclanthology.org/2024.findings-naacl.119/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
88. [RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners](https://aclanthology.org/2024.lrec-main.1183/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
89. [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*
90. [ReFT: Representation Finetuning for Language Models](https://arxiv.org/abs/2404.03592) *— 2024 · arXiv:2404.03592*
91. [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)*
92. [RevOrder: A Novel Method for Enhanced Arithmetic in Language Models](https://arxiv.org/abs/2402.03822) *— 2024 · arXiv:2402.03822*
93. [SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://arxiv.org/abs/2401.07950) *— 2024 · arXiv:2401.07950*
94. [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*
95. [Sparse Matrix in Large Language Model Fine-tuning](https://arxiv.org/abs/2405.15525) *— 2024 · arXiv:2405.15525*
96. [Subjective Topic meets LLMs: Unleashing Comprehensive, Reflective and Creative Thinking through the Negation of Negation](https://aclanthology.org/2024.emnlp-main.686/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
97. [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)*
98. [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)*
99. [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*
100. [Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://arxiv.org/abs/2402.05376) *— 2024 · arXiv:2402.05376*

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/page/3/)