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home / docs / papers / cited-by / Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 4

Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 4

585 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. 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)
  2. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity — 2023 · arXiv:2302.04023
  3. Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models — 2023 · arXiv:2308.10379
  4. Aligning Language Models with Preferences through f-divergence Minimization — 2023 · arXiv:2302.08215
  5. Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training — 2023 · arXiv:2309.17179
  6. Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions — 2023 · arXiv:2307.13339
  7. Answering Questions by Meta-Reasoning over Multiple Chains of Thought — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  8. AutoML-GPT: Automatic Machine Learning with GPT — 2023 · arXiv:2305.02499
  9. Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  10. Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies — 2023 · arXiv:2308.03188
  11. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models — 2023 · arXiv:2312.06585
  12. Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  13. CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society — 2023 · arXiv:2303.17760
  14. CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  15. Can GPT-3 Perform Statutory Reasoning? — 2023 · arXiv:2302.06100
  16. Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  17. Certified Deductive Reasoning with Language Models — 2023 · arXiv:2306.04031
  18. Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  19. Chain-of-Thought Reasoning is a Policy Improvement Operator — 2023 · arXiv:2309.08589
  20. Challenges and Applications of Large Language Models — 2023 · arXiv:2307.10169
  21. ChemCrow: Augmenting large-language models with chemistry tools — 2023 · arXiv:2304.05376
  22. CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
  23. Cognitive Architectures for Language Agents — 2023 · arXiv:2309.02427
  24. Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  25. Crystal: Introspective Reasoners Reinforced with Self-Feedback — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  26. Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  27. DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines — 2023 · arXiv:2310.03714
  28. Democratizing Reasoning Ability: Tailored Learning from Large Language Model — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  29. DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  30. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  31. Don''t Add, don''t Miss: Effective Content Preserving Generation from Pre-Selected Text Spans — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  32. Evaluating Cognitive Maps and Planning in Large Language Models with CogEval — 2023 · arXiv:2309.15129
  33. Evaluating Language Models for Mathematics through Interactions — 2023 · arXiv:2306.01694
  34. Explain-then-translate: an analysis on improving program translation with self-generated explanations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  35. Explanation Regeneration via Information Bottleneck — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  36. Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  37. Exploring the Effectiveness of Prompt Engineering for Legal Reasoning Tasks — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  38. Faithful Question Answering with Monte-Carlo Planning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  39. FireAct: Toward Language Agent Fine-tuning — 2023 · arXiv:2310.05915
  40. From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought — 2023 · arXiv:2306.12672
  41. 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)
  42. Hypothesis Search: Inductive Reasoning with Language Models — 2023 · arXiv:2309.05660
  43. Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing — 2023 · arXiv:2305.16635
  44. Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  45. Improving Factuality and Reasoning in Language Models through Multiagent Debate — 2023 · arXiv:2305.14325
  46. 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)
  47. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? — 2023 · arXiv:2302.06476
  48. Knowledge Corpus Error in Question Answering — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  49. Knowledge Rumination for Pre-trained Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  50. 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)
  51. 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)
  52. LLM-Assisted Code Cleaning For Training Accurate Code Generators — 2023 · arXiv:2311.14904
  53. Language Modeling with Latent Situations — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  54. Language Models Don''t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting — 2023 · arXiv:2305.04388
  55. Language Models can Solve Computer Tasks — 2023 · arXiv:2303.17491
  56. Large Language Model Guided Tree-of-Thought — 2023 · arXiv:2305.08291
  57. Large Language Model Programs — 2023 · arXiv:2305.05364
  58. Large Language Models Can Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  59. Learning to Reason and Memorize with Self-Notes — 2023 · arXiv:2305.00833
  60. Let's Verify Step by Step — 2023 · arXiv:2305.20050
  61. Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  62. LogiCoT: Logical Chain-of-Thought Instruction Tuning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  63. MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  64. 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)
  65. Mixed Distillation Helps Smaller Language Model Better Reasoning — 2023 · arXiv:2312.10730
  66. MoT: Memory-of-Thought Enables ChatGPT to Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  67. Natural Language Reasoning, A Survey — 2023 · arXiv:2303.14725
  68. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future — 2023 · arXiv:2309.15402
  69. Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions — 2023 · arXiv:2212.10561
  70. 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)
  71. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution — 2023 · arXiv:2309.16797
  72. Pushing the Limits of ChatGPT on NLP Tasks — 2023 · arXiv:2306.09719
  73. Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency — 2023 · arXiv:2309.17382
  74. Reinforced Self-Training (ReST) for Language Modeling — 2023 · arXiv:2308.08998
  75. Rethinking with Retrieval: Faithful Large Language Model Inference — 2023 · arXiv:2301.00303
  76. STEER: Unified Style Transfer with Expert Reinforcement — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  77. Scaling Relationship on Learning Mathematical Reasoning with Large Language Models — 2023 · arXiv:2308.01825
  78. Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  79. 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
  80. Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation — 2023 · arXiv:2307.15337
  81. Strategic Reasoning with Language Models — 2023 · arXiv:2305.19165
  82. 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)
  83. TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  84. 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
  85. Think before you speak: Training Language Models With Pause Tokens — 2023 · arXiv:2310.02226
  86. 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)
  87. Through the Lens of Core Competency: Survey on Evaluation of Large Language Models — 2023 · arXiv:2308.07902
  88. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving — 2023 · arXiv:2309.17452
  89. Toolformer: Language Models Can Teach Themselves to Use Tools — 2023 · arXiv:2302.04761
  90. Towards Reasoning in Large Language Models: A Survey — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  91. Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models — 2023 · arXiv:2303.04671
  92. Why think step by step? Reasoning emerges from the locality of experience — 2023 · arXiv:2304.03843
  93. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct — 2023 · arXiv:2308.09583
  94. ZARA: Improving Few-Shot Self-Rationalization for Small Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  95. Can language models learn from explanations in context? — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  96. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — 2022 · arXiv:2201.11903
  97. Compositional Semantic Parsing with Large Language Models — 2022 · arXiv:2209.15003
  98. Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP — 2022 · arXiv:2212.14024
  99. Does Self-Rationalization Improve Robustness to Spurious Correlations? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  100. Explanations from Large Language Models Make Small Reasoners Better — 2022 · arXiv:2210.06726

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