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Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 6

592 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

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  1. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? — 2023 · arXiv:2302.06476
  2. It Ain''t Over: A Multi-aspect Diverse Math Word Problem Dataset — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  3. KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  4. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  5. Language Models can Solve Computer Tasks — 2023 · arXiv:2303.17491
  6. Large Language Models Can Be Easily Distracted by Irrelevant Context — 2023 · arXiv:2302.00093
  7. Large Language Models Can Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  8. Large Language Models are Better Reasoners with Self-Verification — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  9. Learning From Mistakes Makes LLM Better Reasoner — 2023 · arXiv:2310.20689
  10. Learning Multi-Step Reasoning by Solving Arithmetic Tasks — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
  11. Learning by Analogy: Diverse Questions Generation in Math Word Problem — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  12. Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  13. Let''s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  14. Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  15. MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning — 2023 · arXiv:2309.05653
  16. Making Language Models Better Reasoners with Step-Aware Verifier — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  17. Making Presentation Math Computable: A Context-Sensitive Approach for Translating LaTeX to Computer Algebra Systems — 2023 · monograph
  18. Math Word Problem Solving by Generating Linguistic Variants of Problem Statements — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
  19. MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning — 2023 · arXiv:2310.03731
  20. MathPile: A Billion-Token-Scale Pretraining Corpus for Math — 2023 · arXiv:2312.17120
  21. Mathematics, word problems, common sense, and artificial intelligence — 2023 · arXiv:2301.09723
  22. Mixed Distillation Helps Smaller Language Model Better Reasoning — 2023 · arXiv:2312.10730
  23. Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models — 2023 · arXiv:2305.14705
  24. NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  25. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future — 2023 · arXiv:2309.15402
  26. Neural Machine Translation for Mathematical Formulae — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  27. Non-Autoregressive Math Word Problem Solver with Unified Tree Structure — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  28. Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  29. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  30. Progressive-Hint Prompting Improves Reasoning in Large Language Models — 2023 · arXiv:2304.09797
  31. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution — 2023 · arXiv:2309.16797
  32. PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  33. R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  34. REFINER: Reasoning Feedback on Intermediate Representations — 2023 · arXiv:2304.01904
  35. Reasoning in Large Language Models Through Symbolic Math Word Problems — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  36. Retrieving Multimodal Information for Augmented Generation: A Survey — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  37. Scaling Relationship on Learning Mathematical Reasoning with Large Language Models — 2023 · arXiv:2308.01825
  38. Self-Evaluation Guided Beam Search for Reasoning — 2023 · arXiv:2305.00633
  39. Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  40. SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data — 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
  41. TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  42. Tab-CoT: Zero-shot Tabular Chain of Thought — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  43. Techniques to Improve Neural Math Word Problem Solvers — 2023 · arXiv:2302.03145
  44. Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems — 2023 · arXiv:2308.05713
  45. The Flan Collection: Designing Data and Methods for Effective Instruction Tuning — 2023 · arXiv:2301.13688
  46. TheoremQA: A Theorem-driven Question Answering Dataset — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  47. Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  48. TinyGSM: achieving >80% on GSM8k with small language models — 2023 · arXiv:2312.09241
  49. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving — 2023 · arXiv:2309.17452
  50. Toolformer: Language Models Can Teach Themselves to Use Tools — 2023 · arXiv:2302.04761
  51. Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration — 2023 · arXiv:2311.08152
  52. Towards Reasoning in Large Language Models: A Survey — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  53. UniMath: A Foundational and Multimodal Mathematical Reasoner — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  54. Unleashing the potential of prompt engineering for large language models — 2023 · arXiv:2310.14735 · Patterns 6(6) 101260 (2025)
  55. What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  56. World Models for Math Story Problems — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  57. WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models — 2023 · arXiv:2311.15930
  58. ArMATH: a Dataset for Solving Arabic Math Word Problems — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  59. Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
  60. Automatic Generation of Socratic Subquestions for Teaching Math Word Problems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  61. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models — 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj
  62. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — 2022 · arXiv:2201.11903
  63. Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning — 2022 · arXiv:2209.14610
  64. Generating Sequences by Learning to Self-Correct — 2022 · arXiv:2211.00053
  65. HAWP: a Dataset for Hindi Arithmetic Word Problem Solving — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  66. Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  67. Is a Question Decomposition Unit All We Need? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  68. LILA: A Unified Benchmark for Mathematical Reasoning — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  69. Language models show human-like content effects on reasoning tasks — 2022 · arXiv:2207.07051
  70. Large language models are zero-shot reasoners — 2022
  71. Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction — our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  72. MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers — 2022 · AAAI 2022 demo (arXiv 2109.00799)
  73. Mind''s Eye: Grounded Language Model Reasoning through Simulation — 2022 · arXiv:2210.05359
  74. Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  75. Noun-MWP: Math Word Problems Meet Noun Answers — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  76. PAL: Program-aided Language Models — 2022 · arXiv:2211.10435
  77. PaLM: Scaling Language Modeling with Pathways — 2022 · arXiv:2204.02311
  78. PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change — 2022 · arXiv:2206.10498
  79. Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  80. Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks — 2022 · arXiv:2211.12588
  81. Reasoning Like Program Executors — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  82. Reasoning with Language Model Prompting: A Survey — 2022 · arXiv:2212.09597
  83. Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  84. Self-Consistency Improves Chain of Thought Reasoning in Language Models — 2022 · arXiv:2203.11171
  85. Textual Enhanced Contrastive Learning for Solving Math Word Problems — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  86. Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  87. UL2: Unifying Language Learning Paradigms — 2022 · arXiv:2205.05131
  88. Unbiased Math Word Problems Benchmark for Mitigating Solving Bias — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
  89. Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers — 2022 · arXiv 2205.15683
  90. NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
  91. Representing Numbers in NLP: a Survey and a Vision — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  92. Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems — 2021 · arXiv 2111.05364

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