---
title: "Who cites Solving General Arithmetic Word Problems"
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/"
---
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# Who cites «Solving General Arithmetic Word Problems»

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

1. [Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs](https://aclanthology.org/2026.acl-long.537/) *— 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. [AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios](https://aclanthology.org/2026.acl-long.380/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Benchmarking Mathematical Reasoning in a Low-Resource Language: Structured Prompting and Evaluation in Basque](https://aclanthology.org/2026.lrec-1.412/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
5. [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)*
6. [CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models](https://arxiv.org/abs/2605.28292) *— 2026 · arXiv:2605.28292*
7. [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*
8. [Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation](https://arxiv.org/abs/2606.05988) *— 2026 · arXiv:2606.05988*
9. [Dynamic PMI-Guided Contrastive Decoding Reduces Hallucination in Large Language Models: A Unified Framework of Fine-Grained Input Transformations](https://aclanthology.org/2026.findings-acl.1212/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
10. [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*
11. [From Images to Words: Efficient Cross-Modal Knowledge Distillation to Language Models from Black-box Teachers](https://arxiv.org/abs/2603.10877) *— 2026 · arXiv:2603.10877*
12. [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*
13. [GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering](https://aclanthology.org/2026.findings-acl.319/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
14. [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*
15. [Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization](https://aclanthology.org/2026.findings-acl.1010/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
16. [Inference-Time Rethinking with Latent Thought Vectors for Math Reasoning](https://arxiv.org/abs/2602.06584) *— 2026 · arXiv:2602.06584*
17. [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*
18. [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*
19. [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)*
20. [More Agents Improve Math Problem Solving but Adversarial Robustness Gap Persists](https://aclanthology.org/2026.findings-acl.2158/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
21. [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*
22. [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*
23. [Pin Once, Swap Light: Subspace-Aligned Centroid-Residual Training for Efficient Ultra-LoRA Serving](https://arxiv.org/abs/2608.03579) *— 2026 · arXiv:2608.03579*
24. [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)*
25. [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)*
26. [Task Competence Is Not Instruction Following: Evaluating Instruction-Conflicting Behavior in Small Language Models](https://arxiv.org/abs/2607.19608) *— 2026 · arXiv:2607.19608*
27. [TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.207/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
28. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
29. [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*
30. [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)*
31. [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)*
32. [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)*
33. [AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration](https://aclanthology.org/2025.findings-emnlp.636/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
34. [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*
35. [ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving](https://arxiv.org/abs/2501.08203) *— 2025 · arXiv:2501.08203*
36. [Audit-of-Understanding: Posterior-Constrained Inference for Mathematical Reasoning in Language Models](https://arxiv.org/abs/2510.10252) *— 2025 · arXiv:2510.10252*
37. [Automatic Mathematic In-Context Example Generation for LLM Using Multi-Modal Consistency](https://aclanthology.org/2025.coling-main.597/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
38. [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*
39. [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)*
40. [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*
41. [CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs](https://aclanthology.org/2025.emnlp-main.704/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
42. [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*
43. [CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning](https://aclanthology.org/2025.emnlp-main.1024/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
44. [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*
45. [CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models](https://aclanthology.org/2025.findings-emnlp.168/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
46. [Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting](https://aclanthology.org/2025.naacl-industry.52/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)*
47. [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*
48. [DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning](https://aclanthology.org/2025.naacl-long.96/) *— 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)*
49. [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*
50. [Do Large Language Model Benchmarks Test Reliability?](https://arxiv.org/abs/2502.03461) *— 2025 · arXiv:2502.03461*
51. [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*
52. [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*
53. [Evaluating Frontier LLMs on PhD-Level Mathematical Reasoning: A Benchmark on a Textbook in Theoretical Computer Science about Randomized Algorithms](https://arxiv.org/abs/2512.13978) *— 2025 · arXiv:2512.13978*
54. [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)*
55. [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*
56. [Exploring Backward Reasoning in Large Language Models](https://aclanthology.org/2025.findings-naacl.366/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
57. [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*
58. [Fast Thinking with Structured Prompts: Enabling LLM Reasoning without Chain-of-Thought Generation](https://aclanthology.org/2025.ranlp-1.87/) *— 2025 · Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era*
59. [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*
60. [GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems](https://aclanthology.org/2025.emnlp-industry.106/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track*
61. [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*
62. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
63. [Learning Text Styles: A Study on Transfer, Attribution, and Verification](https://arxiv.org/abs/2507.16530) *— 2025 · arXiv:2507.16530*
64. [LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs](https://aclanthology.org/2025.findings-acl.1247/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
65. [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*
66. [Local Prompt Optimization](https://aclanthology.org/2025.naacl-short.7/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
67. [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*
68. [Mitigating Demonstration Bias through Global Coevolutionary Reasoning](https://aclanthology.org/2025.findings-acl.450/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
69. [NAT: Enhancing Agent Tuning with Negative Samples](https://aclanthology.org/2025.naacl-long.378/) *— 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)*
70. [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*
71. [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)*
72. [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*
73. [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*
74. [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)*
75. [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*
76. [Propulsion: Steering LLM with Tiny Fine-Tuning](https://aclanthology.org/2025.coling-main.506/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
77. [Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability](https://aclanthology.org/2025.findings-acl.302/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
78. [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)*
79. [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*
80. [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)*
81. [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*
82. [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*
83. [Syntactic Blind Spots: How Misalignment Leads to LLMs” Mathematical Errors](https://aclanthology.org/2025.mathnlp-main.1/) *— 2025 · Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)*
84. [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*
85. [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)*
86. [Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains](https://arxiv.org/abs/2505.16552) *— 2025 · arXiv:2505.16552*
87. [ThoughtProbe: Classifier-Guided LLM Thought Space Exploration via Probing Representations](https://aclanthology.org/2025.emnlp-main.307/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
88. [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*
89. [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*
90. [Towards Faithful Multi-step Reasoning through Fine-Grained Causal-aware Attribution Reasoning Distillation](https://aclanthology.org/2025.coling-main.157/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
91. [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://arxiv.org/abs/2501.09686) *— 2025 · arXiv:2501.09686*
92. [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*
93. [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)*
94. [Uncertainty-Aware Contrastive Decoding](https://aclanthology.org/2025.findings-acl.1352/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
95. [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*
96. [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*
97. [“Well, Keep Thinking”: Enhancing LLM Reasoning with Adaptive Injection Decoding](https://aclanthology.org/2025.findings-acl.520/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
98. [A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages](https://aclanthology.org/2024.findings-naacl.78/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
99. [AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning](https://aclanthology.org/2024.emnlp-main.458/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
100. [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*

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