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# Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 3

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. [Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models](https://aclanthology.org/2024.findings-emnlp.109/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
2. [Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning](https://aclanthology.org/2024.emnlp-main.817/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
3. [Learning to Maximize Mutual Information for Chain-of-Thought Distillation](https://aclanthology.org/2024.findings-acl.409/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
4. [Learning to Paraphrase for Alignment with LLM Preference](https://aclanthology.org/2024.findings-emnlp.134/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
5. [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)*
6. [LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models](https://aclanthology.org/2024.emnlp-main.128/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
7. [LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models](https://aclanthology.org/2024.acl-long.739/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization](https://aclanthology.org/2024.acl-long.539/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations](https://aclanthology.org/2024.findings-emnlp.696/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
10. [MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks](https://aclanthology.org/2024.eacl-long.179/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [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)*
12. [MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions](https://arxiv.org/abs/2405.19444) *— 2024 · arXiv:2405.19444*
13. [MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs](https://aclanthology.org/2024.acl-long.151/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Mentor-KD: Making Small Language Models Better Multi-step Reasoners](https://aclanthology.org/2024.emnlp-main.977/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
15. [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://arxiv.org/abs/2405.12205) *— 2024 · arXiv:2405.12205*
16. [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)*
17. [Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures](https://arxiv.org/abs/2407.19580) *— 2024 · arXiv:2407.19580*
18. [Mirror-Consistency: Harnessing Inconsistency in Majority Voting](https://aclanthology.org/2024.findings-emnlp.135/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
19. [Mixed Distillation Helps Smaller Language Models Reason Better](https://aclanthology.org/2024.findings-emnlp.91/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
20. [MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning](https://aclanthology.org/2024.emnlp-main.274/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
21. [MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning](https://aclanthology.org/2024.acl-long.551/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Multi-Step Reasoning with Large Language Models, a Survey](https://arxiv.org/abs/2407.11511) *— 2024 · arXiv:2407.11511*
23. [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195) *— 2024 · arXiv:2408.06195*
24. [Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives](https://aclanthology.org/2024.findings-emnlp.963/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
25. [Neuro-Symbolic Data Generation for Math Reasoning](https://arxiv.org/abs/2412.04857) *— 2024 · arXiv:2412.04857*
26. [NumHG: A Dataset for Number-Focused Headline Generation](https://aclanthology.org/2024.lrec-main.1078/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
27. [Number Cookbook: Number Understanding of Language Models and How to Improve It](https://arxiv.org/abs/2411.03766) *— 2024 · arXiv:2411.03766*
28. [OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset](https://arxiv.org/abs/2402.10176) *— 2024 · arXiv:2402.10176*
29. [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/abs/2402.14830) *— 2024 · arXiv:2402.14830*
30. [PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning](https://aclanthology.org/2024.acl-long.379/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning](https://aclanthology.org/2024.naacl-long.142/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
32. [Personality Alignment of Large Language Models](https://arxiv.org/abs/2408.11779) *— 2024 · arXiv:2408.11779*
33. [Plum: Prompt Learning using Metaheuristics](https://aclanthology.org/2024.findings-acl.129/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
34. [Prompt Optimization via Adversarial In-Context Learning](https://aclanthology.org/2024.acl-long.395/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
35. [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*
36. [Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts](https://aclanthology.org/2024.emnlp-main.408/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
37. [Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness](https://aclanthology.org/2024.acl-long.283/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [RESPROMPT: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models](https://aclanthology.org/2024.naacl-long.323/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
39. [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)*
40. [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*
41. [ReFT: Reasoning with Reinforced Fine-Tuning](https://aclanthology.org/2024.acl-long.410/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [ReFT: Representation Finetuning for Language Models](https://arxiv.org/abs/2404.03592) *— 2024 · arXiv:2404.03592*
43. [Reranking Laws for Language Generation: A Communication-Theoretic Perspective](https://arxiv.org/abs/2409.07131) *— 2024 · arXiv:2409.07131*
44. [ResLoRA: Identity Residual Mapping in Low-Rank Adaption](https://aclanthology.org/2024.findings-acl.525/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
45. [Retrieved In-Context Principles from Previous Mistakes](https://aclanthology.org/2024.emnlp-main.465/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
46. [Rho-1: Not All Tokens Are What You Need](https://arxiv.org/abs/2404.07965) *— 2024 · arXiv:2404.07965*
47. [RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning](https://aclanthology.org/2024.emnlp-main.57/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
48. [SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models](https://arxiv.org/abs/2408.15565) *— 2024 · arXiv:2408.15565*
49. [SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark](https://aclanthology.org/2024.acl-short.11/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
50. [SciCode: A Research Coding Benchmark Curated by Scientists](https://arxiv.org/abs/2407.13168) *— 2024 · arXiv:2407.13168*
51. [SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://arxiv.org/abs/2401.07950) *— 2024 · arXiv:2401.07950*
52. [Self-Consistency Boosts Calibration for Math Reasoning](https://aclanthology.org/2024.findings-emnlp.349/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
53. [Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives](https://aclanthology.org/2024.acl-long.197/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning](https://aclanthology.org/2024.acl-long.643/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
55. [SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials](https://aclanthology.org/2024.semeval-1.271/) *— 2024 · Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)*
56. [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://aclanthology.org/2024.naacl-industry.34/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)*
57. [Solving General Natural-Language-Description Optimization Problems with Large Language Models](https://aclanthology.org/2024.naacl-industry.42/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)*
58. [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*
59. [Sparse Matrix in Large Language Model Fine-tuning](https://arxiv.org/abs/2405.15525) *— 2024 · arXiv:2405.15525*
60. [Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling](https://arxiv.org/abs/2410.11325) *— 2024 · arXiv:2410.11325*
61. [SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs](https://aclanthology.org/2024.acl-long.17/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [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)*
63. [Task Oriented In-Domain Data Augmentation](https://aclanthology.org/2024.emnlp-main.1162/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
64. [Teaching Large Language Models to Reason with Reinforcement Learning](https://arxiv.org/abs/2403.04642) *— 2024 · arXiv:2403.04642*
65. [Teaching Small Language Models Reasoning through Counterfactual Distillation](https://aclanthology.org/2024.emnlp-main.333/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
66. [The Impact of Reasoning Step Length on Large Language Models](https://aclanthology.org/2024.findings-acl.108/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
67. [ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval](https://aclanthology.org/2024.lrec-main.1413/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
68. [Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Model](https://aclanthology.org/2024.naacl-long.48/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
69. [Toward Adaptive Reasoning in Large Language Models with Thought Rollback](https://arxiv.org/abs/2412.19707) *— 2024 · arXiv:2412.19707*
70. [Towards A Unified View of Answer Calibration for Multi-Step Reasoning](https://aclanthology.org/2024.nlrse-1.3/) *— 2024 · Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)*
71. [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://arxiv.org/abs/2402.01622) *— 2024 · arXiv:2402.01622*
72. [What Makes Math Word Problems Challenging for LLMs?](https://aclanthology.org/2024.findings-naacl.72/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
73. [When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives](https://aclanthology.org/2024.emnlp-main.246/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
74. [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*
75. [Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://arxiv.org/abs/2402.05376) *— 2024 · arXiv:2402.05376*
76. [mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models](https://aclanthology.org/2024.acl-long.649/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [面向小规模大语言模型推理优化的推理路径排序方法(A Reasoning Paths Ranking Method for Reasoning Optimization of Small-scale Large Language Models)](https://aclanthology.org/2024.ccl-1.72/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
78. [A Categorical Archive of ChatGPT Failures](https://arxiv.org/abs/2302.03494) *— 2023 · arXiv:2302.03494*
79. [A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models](https://aclanthology.org/2023.acl-long.32/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
80. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
81. [A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis](https://arxiv.org/abs/2307.12856) *— 2023 · arXiv:2307.12856*
82. [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) *— 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)*
83. [ARB: Advanced Reasoning Benchmark for Large Language Models](https://arxiv.org/abs/2307.13692) *— 2023 · arXiv:2307.13692*
84. [ART: Automatic multi-step reasoning and tool-use for large language models](https://arxiv.org/abs/2303.09014) *— 2023 · arXiv:2303.09014*
85. [Active Prompting with Chain-of-Thought for Large Language Models](https://arxiv.org/abs/2302.12246) *— 2023 · arXiv:2302.12246*
86. [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://arxiv.org/abs/2310.03710) *— 2023 · arXiv:2310.03710*
87. [Agent Lumos: Unified and Modular Training for Open-Source Language Agents](https://arxiv.org/abs/2311.05657) *— 2023 · arXiv:2311.05657*
88. [An Expression Tree Decoding Strategy for Mathematical Equation Generation](https://aclanthology.org/2023.emnlp-main.29/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
89. [Automatic Model Selection with Large Language Models for Reasoning](https://aclanthology.org/2023.findings-emnlp.55/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
90. [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://aclanthology.org/2023.findings-emnlp.811/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
91. [Batch Prompting: Efficient Inference with Large Language Model APIs](https://aclanthology.org/2023.emnlp-industry.74/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track*
92. [Benchmarks for Automated Commonsense Reasoning: A Survey](https://arxiv.org/abs/2302.04752) *— 2023 · arXiv:2302.04752*
93. [Better Zero-Shot Reasoning with Role-Play Prompting](https://arxiv.org/abs/2308.07702) *— 2023 · arXiv:2308.07702*
94. [Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish](https://aclanthology.org/2023.resourceful-1.13/) *— 2023 · Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)*
95. [CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models](https://aclanthology.org/2023.findings-emnlp.462/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
96. [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://arxiv.org/abs/2305.11738) *— 2023 · arXiv:2305.11738*
97. [Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems](https://aclanthology.org/2023.emnlp-main.742/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
98. [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://arxiv.org/abs/2306.13063) *— 2023 · arXiv:2306.13063*
99. [Can Language Models Solve Graph Problems in Natural Language?](https://arxiv.org/abs/2305.10037) *— 2023 · arXiv:2305.10037*
100. [Chain of Thought Prompting Elicits Knowledge Augmentation](https://aclanthology.org/2023.findings-acl.408/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*

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