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home / docs / papers / cited-by / Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 2

Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 2

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. Nudging: Inference-time Alignment of LLMs via Guided Decoding — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  2. On the Generalization vs Fidelity Paradox in Knowledge Distillation — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  3. Persona is a Double-Edged Sword: Rethinking the Impact of Role-play Prompts in Zero-shot Reasoning Tasks — 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
  4. PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems — 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
  5. Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  6. Probing for Arithmetic Errors in Language Models — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  7. Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  8. PromptWizard: Optimizing Prompts via Task-Aware, Feedback-Driven Self-Evolution — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  9. Propulsion: Steering LLM with Tiny Fine-Tuning — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
  10. QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  11. Reasoning with Latent Thoughts: On the Power of Looped Transformers — 2025 · arXiv:2502.17416
  12. RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  13. SIKeD: Self-guided Iterative Knowledge Distillation for Mathematical Reasoning — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  14. Self-Harmonized Chain of Thought — 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)
  15. Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  16. Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  17. Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  18. Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  19. Superficial Self-Improved Reasoners Benefit from Model Merging — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  20. Theorem Prover as a Judge for Synthetic Data Generation — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  21. Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains — 2025 · arXiv:2505.16552
  22. Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  23. Token-Efficient RL for LLM Reasoning — 2025 · arXiv:2504.20834
  24. Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
  25. Towards Robust Mathematical Reasoning — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  26. UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  27. UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  28. Uncertainty-Aware Contrastive Decoding — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  29. Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap — 2025 · Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)
  30. Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  31. Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  32. VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  33. Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  34. Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors — 2025 · arXiv:2504.04785
  35. When natural language is not enough: The limits of in-context learning demonstrations in multilingual reasoning — 2025 · Findings of the Association for Computational Linguistics: NAACL 2025
  36. Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
  37. 基于思维链和知识迁移的多语言问答推理研究 — 2025 · Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
  38. 3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability — 2024 · arXiv:2409.00119
  39. A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  40. API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  41. Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  42. Advancing LLM Reasoning Generalists with Preference Trees — 2024 · arXiv:2404.02078
  43. Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  44. Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models — 2024 · arXiv:2406.02061
  45. AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  46. An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning — 2024 · arXiv:2403.00799
  47. ApiQ: Finetuning of 2-Bit Quantized Large Language Model — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  48. Ask Again, Then Fail: Large Language Models'' Vacillations in Judgment — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  49. Automated Design of Agentic Systems — 2024 · arXiv:2408.08435
  50. Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  51. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws — 2024 · arXiv:2401.00448
  52. Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  53. Boosting Language Models Reasoning with Chain-of-Knowledge Prompting — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  54. Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  55. COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training — 2024 · arXiv:2410.19313
  56. Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages — 2024 · Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation
  57. Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  58. Common 7B Language Models Already Possess Strong Math Capabilities — 2024 · arXiv:2403.04706
  59. Competition-Level Problems are Effective LLM Evaluators — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  60. ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  61. ControlMath: Controllable Data Generation Promotes Math Generalist Models — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  62. Cutting Through the Noise: Boosting LLM Performance on Math Word Problems — our card · 2024 · arXiv:2406.15444
  63. DataComp-LM: In search of the next generation of training sets for language models — 2024 · arXiv:2406.11794
  64. DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence — 2024 · arXiv:2401.14196
  65. Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models — 2024 · arXiv:2411.01141
  66. DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  67. DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  68. EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
  69. Empowering Multi-step Reasoning across Languages via Program-Aided Language Models — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  70. Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  71. Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning — 2024 · arXiv:2401.10480
  72. Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  73. Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  74. Explicit Memory Learning with Expectation Maximization — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  75. Exploring Reversal Mathematical Reasoning Ability for Large Language Models — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  76. Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  77. Fewer is More: Boosting Math Reasoning with Reinforced Context Pruning — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  78. FinanceMATH: Knowledge-Intensive Math Reasoning in Finance Domains — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  79. Forward-Backward Reasoning in Large Language Models for Mathematical Verification — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  80. From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting — 2024 · Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)
  81. GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution — 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
  82. GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements — 2024 · arXiv:2402.10963
  83. GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  84. Give me a hint: Can LLMs take a hint to solve math problems? — 2024 · arXiv:2410.05915
  85. Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing — 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP
  86. Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  87. How Do Humans Write Code? Large Models Do It the Same Way Too — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  88. How to Train Data-Efficient LLMs — 2024 · arXiv:2402.09668
  89. Improving Language Model Reasoning with Self-motivated Learning — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  90. Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  91. Instructing Large Language Models to Identify and Ignore Irrelevant Conditions — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  92. Interpreting and Improving Large Language Models in Arithmetic Calculation — 2024 · arXiv:2409.01659
  93. Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  94. JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models — 2024 · arXiv:2405.14365
  95. Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  96. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning — 2024 · arXiv:2403.02333
  97. Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  98. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models — 2024 · Transactions of the Association for Computational Linguistics
  99. LangBridge: Multilingual Reasoning Without Multilingual Supervision — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  100. Large Language Models for Mathematical Reasoning: Progresses and Challenges — 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

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