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home / docs / papers / cited-by / Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 3

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

Pages: 1 · 2 · 3 · 4 · 5 · 6

  1. Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  2. Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  3. Learning to Maximize Mutual Information for Chain-of-Thought Distillation — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  4. Learning to Paraphrase for Alignment with LLM Preference — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  5. LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models — 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 — 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 — 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 — 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 — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  10. MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks — 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 — 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 — 2024 · arXiv:2405.19444
  13. MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs — 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 — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  15. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving — 2024 · arXiv:2405.12205
  16. MinT: Boosting Generalization in Mathematical Reasoning via Multi-view Fine-tuning — 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 — 2024 · arXiv:2407.19580
  18. Mirror-Consistency: Harnessing Inconsistency in Majority Voting — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  19. Mixed Distillation Helps Smaller Language Models Reason Better — 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 — 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 — 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 — 2024 · arXiv:2407.11511
  23. Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers — 2024 · arXiv:2408.06195
  24. Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  25. Neuro-Symbolic Data Generation for Math Reasoning — 2024 · arXiv:2412.04857
  26. NumHG: A Dataset for Number-Focused Headline Generation — 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 — 2024 · arXiv:2411.03766
  28. OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset — 2024 · arXiv:2402.10176
  29. Orca-Math: Unlocking the potential of SLMs in Grade School Math — 2024 · arXiv:2402.14830
  30. PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning — 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 — 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 — 2024 · arXiv:2408.11779
  33. Plum: Prompt Learning using Metaheuristics — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  34. Prompt Optimization via Adversarial In-Context Learning — 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 — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  36. Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts — 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 — 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 — 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 — 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 — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  41. ReFT: Reasoning with Reinforced Fine-Tuning — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  42. ReFT: Representation Finetuning for Language Models — 2024 · arXiv:2404.03592
  43. Reranking Laws for Language Generation: A Communication-Theoretic Perspective — 2024 · arXiv:2409.07131
  44. ResLoRA: Identity Residual Mapping in Low-Rank Adaption — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  45. Retrieved In-Context Principles from Previous Mistakes — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  46. Rho-1: Not All Tokens Are What You Need — 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 — 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 — 2024 · arXiv:2408.15565
  49. SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark — 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 — 2024 · arXiv:2407.13168
  51. SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models — 2024 · arXiv:2401.07950
  52. Self-Consistency Boosts Calibration for Math Reasoning — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  53. Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives — 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 — 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 — 2024 · Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
  56. Shears: Unstructured Sparsity with Neural Low-rank Adapter Search — 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 — 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? — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  59. Sparse Matrix in Large Language Model Fine-tuning — 2024 · arXiv:2405.15525
  60. Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling — 2024 · arXiv:2410.11325
  61. SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs — 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 — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  63. Task Oriented In-Domain Data Augmentation — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  64. Teaching Large Language Models to Reason with Reinforcement Learning — 2024 · arXiv:2403.04642
  65. Teaching Small Language Models Reasoning through Counterfactual Distillation — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  66. The Impact of Reasoning Step Length on Large Language Models — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  67. ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval — 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 — 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 — 2024 · arXiv:2412.19707
  70. Towards A Unified View of Answer Calibration for Multi-Step Reasoning — 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 — 2024 · arXiv:2402.01622
  72. What Makes Math Word Problems Challenging for LLMs? — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  73. When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives — 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 — 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 — 2024 · arXiv:2402.05376
  76. mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models — 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) — 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
  78. A Categorical Archive of ChatGPT Failures — 2023 · arXiv:2302.03494
  79. A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models — 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 — 2023 · arXiv:2307.06435
  81. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis — 2023 · arXiv:2307.12856
  82. A Survey of Large Language Models — 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)
  83. ARB: Advanced Reasoning Benchmark for Large Language Models — 2023 · arXiv:2307.13692
  84. ART: Automatic multi-step reasoning and tool-use for large language models — 2023 · arXiv:2303.09014
  85. Active Prompting with Chain-of-Thought for Large Language Models — 2023 · arXiv:2302.12246
  86. Agent Instructs Large Language Models to be General Zero-Shot Reasoners — 2023 · arXiv:2310.03710
  87. Agent Lumos: Unified and Modular Training for Open-Source Language Agents — 2023 · arXiv:2311.05657
  88. An Expression Tree Decoding Strategy for Mathematical Equation Generation — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  89. Automatic Model Selection with Large Language Models for Reasoning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  90. Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  91. Batch Prompting: Efficient Inference with Large Language Model APIs — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
  92. Benchmarks for Automated Commonsense Reasoning: A Survey — 2023 · arXiv:2302.04752
  93. Better Zero-Shot Reasoning with Role-Play Prompting — 2023 · arXiv:2308.07702
  94. Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish — 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 — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  96. CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing — 2023 · arXiv:2305.11738
  97. Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems — 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 — 2023 · arXiv:2306.13063
  99. Can Language Models Solve Graph Problems in Natural Language? — 2023 · arXiv:2305.10037
  100. Chain of Thought Prompting Elicits Knowledge Augmentation — 2023 · Findings of the Association for Computational Linguistics: ACL 2023

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