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Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 6

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

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  1. FireAct: Toward Language Agent Fine-tuning — 2023 · arXiv:2310.05915
  2. From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought — 2023 · arXiv:2306.12672
  3. Graph of Thoughts: Solving Elaborate Problems with Large Language Models — 2023 · arXiv:2308.09687 · Proceedings of the AAAI Conference on Artificial Intelligence 2024 (AAAI'24)
  4. Hypothesis Search: Inductive Reasoning with Language Models — 2023 · arXiv:2309.05660
  5. Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing — 2023 · arXiv:2305.16635
  6. Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  7. Improving Factuality and Reasoning in Language Models through Multiagent Debate — 2023 · arXiv:2305.14325
  8. Interpretable Math Word Problem Solution Generation via Step-by-step Planning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  9. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? — 2023 · arXiv:2302.06476
  10. Knowledge Corpus Error in Question Answering — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  11. Knowledge Rumination for Pre-trained Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  12. LAMBADA: Backward Chaining for Automated Reasoning in Natural Language — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  13. LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers — 2023 · arXiv:2310.15164 (Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5153-5176, Singapore. Association for Computational Linguistics)
  14. LLM-Assisted Code Cleaning For Training Accurate Code Generators — 2023 · arXiv:2311.14904
  15. Language Modeling with Latent Situations — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  16. Language Models Don''t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting — 2023 · arXiv:2305.04388
  17. Language Models can Solve Computer Tasks — 2023 · arXiv:2303.17491
  18. Large Language Model Guided Tree-of-Thought — 2023 · arXiv:2305.08291
  19. Large Language Model Programs — 2023 · arXiv:2305.05364
  20. Large Language Models Can Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  21. Learning to Reason and Memorize with Self-Notes — 2023 · arXiv:2305.00833
  22. Let's Verify Step by Step — 2023 · arXiv:2305.20050
  23. Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  24. LogiCoT: Logical Chain-of-Thought Instruction Tuning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  25. MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  26. 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)
  27. Mixed Distillation Helps Smaller Language Model Better Reasoning — 2023 · arXiv:2312.10730
  28. MoT: Memory-of-Thought Enables ChatGPT to Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  29. Natural Language Reasoning, A Survey — 2023 · arXiv:2303.14725
  30. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future — 2023 · arXiv:2309.15402
  31. Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions — 2023 · arXiv:2212.10561
  32. Pre-trained Language Models Can be Fully Zero-Shot Learners — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  33. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution — 2023 · arXiv:2309.16797
  34. Pushing the Limits of ChatGPT on NLP Tasks — 2023 · arXiv:2306.09719
  35. Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency — 2023 · arXiv:2309.17382
  36. Reasoning Circuits: Few-shot Multi-hop Question Generation with Structured Rationales — 2023 · Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
  37. Reinforced Self-Training (ReST) for Language Modeling — 2023 · arXiv:2308.08998
  38. Rethinking with Retrieval: Faithful Large Language Model Inference — 2023 · arXiv:2301.00303
  39. STEER: Unified Style Transfer with Expert Reinforcement — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  40. Scaling Relationship on Learning Mathematical Reasoning with Large Language Models — 2023 · arXiv:2308.01825
  41. Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  42. Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  43. Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation — 2023 · arXiv:2307.15337
  44. Strategic Reasoning with Language Models — 2023 · arXiv:2305.19165
  45. Symbolic Chain-of-Thought Distillation: Small Models Can Also ``Think'''' Step-by-Step — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  46. TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  47. The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  48. Think before you speak: Training Language Models With Pause Tokens — 2023 · arXiv:2310.02226
  49. ThinkSum: Probabilistic reasoning over sets using large language models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  50. Through the Lens of Core Competency: Survey on Evaluation of Large Language Models — 2023 · arXiv:2308.07902
  51. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving — 2023 · arXiv:2309.17452
  52. Toolformer: Language Models Can Teach Themselves to Use Tools — 2023 · arXiv:2302.04761
  53. Towards Reasoning in Large Language Models: A Survey — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  54. Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models — 2023 · arXiv:2303.04671
  55. Why think step by step? Reasoning emerges from the locality of experience — 2023 · arXiv:2304.03843
  56. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct — 2023 · arXiv:2308.09583
  57. ZARA: Improving Few-Shot Self-Rationalization for Small Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  58. Can language models learn from explanations in context? — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  59. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — 2022 · arXiv:2201.11903
  60. Compositional Semantic Parsing with Large Language Models — 2022 · arXiv:2209.15003
  61. Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP — 2022 · arXiv:2212.14024
  62. Does Self-Rationalization Improve Robustness to Spurious Correlations? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  63. Explanations from Large Language Models Make Small Reasoners Better — 2022 · arXiv:2210.06726
  64. FOLIO: Natural Language Reasoning with First-Order Logic — 2022 · arXiv:2209.00840
  65. Faithful Reasoning Using Large Language Models — 2022 · arXiv:2208.14271
  66. Few-Shot Self-Rationalization with Natural Language Prompts — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
  67. Inferring Implicit Relations in Complex Questions with Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  68. Instruction Induction: From Few Examples to Natural Language Task Descriptions — 2022 · arXiv:2205.10782
  69. Language Model Cascades — 2022 · arXiv:2207.10342
  70. Language models show human-like content effects on reasoning tasks — 2022 · arXiv:2207.07051
  71. Large Language Models Are Human-Level Prompt Engineers — 2022 · arXiv:2211.01910
  72. Large language models are zero-shot reasoners — 2022
  73. Learning by Distilling Context — 2022 · arXiv:2209.15189
  74. Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  75. Legal Prompting: Teaching a Language Model to Think Like a Lawyer — 2022 · arXiv:2212.01326
  76. ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning — 2022 · arXiv:2212.07919
  77. Rationale-Augmented Ensembles in Language Models — 2022 · arXiv:2207.00747
  78. ReAct: Synergizing Reasoning and Acting in Language Models — 2022 · arXiv:2210.03629
  79. Reasoning with Language Model Prompting: A Survey — 2022 · arXiv:2212.09597
  80. Scaling Instruction-Finetuned Language Models — 2022 · arXiv:2210.11416
  81. Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning — 2022 · arXiv:2205.09712
  82. Solving math word problems with process- and outcome-based feedback — 2022 · arXiv:2211.14275
  83. Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks — 2022 · arXiv:2204.02892
  84. Successive Prompting for Decomposing Complex Questions — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  85. Teaching Algorithmic Reasoning via In-context Learning — 2022 · arXiv:2211.09066

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