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# 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](https://md.mova.elamur.ai/free/en/docs/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-3/) · **4** · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-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)](https://aclanthology.org/2024.ccl-1.72/) *— 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](https://arxiv.org/abs/2302.04023) *— 2023 · arXiv:2302.04023*
3. [Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models](https://arxiv.org/abs/2308.10379) *— 2023 · arXiv:2308.10379*
4. [Aligning Language Models with Preferences through f-divergence Minimization](https://arxiv.org/abs/2302.08215) *— 2023 · arXiv:2302.08215*
5. [Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training](https://arxiv.org/abs/2309.17179) *— 2023 · arXiv:2309.17179*
6. [Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions](https://arxiv.org/abs/2307.13339) *— 2023 · arXiv:2307.13339*
7. [Answering Questions by Meta-Reasoning over Multiple Chains of Thought](https://aclanthology.org/2023.emnlp-main.364/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
8. [AutoML-GPT: Automatic Machine Learning with GPT](https://arxiv.org/abs/2305.02499) *— 2023 · arXiv:2305.02499*
9. [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://aclanthology.org/2023.findings-emnlp.811/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
10. [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://arxiv.org/abs/2308.03188) *— 2023 · arXiv:2308.03188*
11. [Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models](https://arxiv.org/abs/2312.06585) *— 2023 · arXiv:2312.06585*
12. [Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval](https://aclanthology.org/2023.findings-emnlp.65/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
13. [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://arxiv.org/abs/2303.17760) *— 2023 · arXiv:2303.17760*
14. [CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data](https://aclanthology.org/2023.emnlp-main.132/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
15. [Can GPT-3 Perform Statutory Reasoning?](https://arxiv.org/abs/2302.06100) *— 2023 · arXiv:2302.06100*
16. [Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model](https://aclanthology.org/2023.findings-emnlp.1036/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
17. [Certified Deductive Reasoning with Language Models](https://arxiv.org/abs/2306.04031) *— 2023 · arXiv:2306.04031*
18. [Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering](https://aclanthology.org/2023.emnlp-main.547/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
19. [Chain-of-Thought Reasoning is a Policy Improvement Operator](https://arxiv.org/abs/2309.08589) *— 2023 · arXiv:2309.08589*
20. [Challenges and Applications of Large Language Models](https://arxiv.org/abs/2307.10169) *— 2023 · arXiv:2307.10169*
21. [ChemCrow: Augmenting large-language models with chemistry tools](https://arxiv.org/abs/2304.05376) *— 2023 · arXiv:2304.05376*
22. [CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification](https://aclanthology.org/2023.eacl-demo.23/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations*
23. [Cognitive Architectures for Language Agents](https://arxiv.org/abs/2309.02427) *— 2023 · arXiv:2309.02427*
24. [Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages](https://aclanthology.org/2023.emnlp-main.163/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
25. [Crystal: Introspective Reasoners Reinforced with Self-Feedback](https://aclanthology.org/2023.emnlp-main.708/) *— 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](https://aclanthology.org/2023.findings-emnlp.806/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
27. [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714) *— 2023 · arXiv:2310.03714*
28. [Democratizing Reasoning Ability: Tailored Learning from Large Language Model](https://aclanthology.org/2023.emnlp-main.120/) *— 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](https://aclanthology.org/2023.emnlp-main.501/) *— 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](https://aclanthology.org/2023.findings-acl.507/) *— 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](https://aclanthology.org/2023.findings-emnlp.852/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
32. [Evaluating Cognitive Maps and Planning in Large Language Models with CogEval](https://arxiv.org/abs/2309.15129) *— 2023 · arXiv:2309.15129*
33. [Evaluating Language Models for Mathematics through Interactions](https://arxiv.org/abs/2306.01694) *— 2023 · arXiv:2306.01694*
34. [Explain-then-translate: an analysis on improving program translation with self-generated explanations](https://aclanthology.org/2023.findings-emnlp.119/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
35. [Explanation Regeneration via Information Bottleneck](https://aclanthology.org/2023.findings-acl.765/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
36. [Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting](https://aclanthology.org/2023.emnlp-main.41/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
37. [Exploring the Effectiveness of Prompt Engineering for Legal Reasoning Tasks](https://aclanthology.org/2023.findings-acl.858/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
38. [Faithful Question Answering with Monte-Carlo Planning](https://aclanthology.org/2023.acl-long.218/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [FireAct: Toward Language Agent Fine-tuning](https://arxiv.org/abs/2310.05915) *— 2023 · arXiv:2310.05915*
40. [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://arxiv.org/abs/2306.12672) *— 2023 · arXiv:2306.12672*
41. [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://arxiv.org/abs/2308.09687) *— 2023 · arXiv:2308.09687 · Proceedings of the AAAI Conference on Artificial Intelligence 2024 (AAAI'24)*
42. [Hypothesis Search: Inductive Reasoning with Language Models](https://arxiv.org/abs/2309.05660) *— 2023 · arXiv:2309.05660*
43. [Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing](https://arxiv.org/abs/2305.16635) *— 2023 · arXiv:2305.16635*
44. [Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming](https://aclanthology.org/2023.findings-acl.191/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
45. [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://arxiv.org/abs/2305.14325) *— 2023 · arXiv:2305.14325*
46. [Interpretable Math Word Problem Solution Generation via Step-by-step Planning](https://aclanthology.org/2023.acl-long.379/) *— 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?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
48. [Knowledge Corpus Error in Question Answering](https://aclanthology.org/2023.findings-emnlp.616/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
49. [Knowledge Rumination for Pre-trained Language Models](https://aclanthology.org/2023.emnlp-main.206/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
50. [LAMBADA: Backward Chaining for Automated Reasoning in Natural Language](https://aclanthology.org/2023.acl-long.361/) *— 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](https://arxiv.org/abs/2310.15164) *— 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](https://arxiv.org/abs/2311.14904) *— 2023 · arXiv:2311.14904*
53. [Language Modeling with Latent Situations](https://aclanthology.org/2023.findings-acl.795/) *— 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](https://arxiv.org/abs/2305.04388) *— 2023 · arXiv:2305.04388*
55. [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) *— 2023 · arXiv:2303.17491*
56. [Large Language Model Guided Tree-of-Thought](https://arxiv.org/abs/2305.08291) *— 2023 · arXiv:2305.08291*
57. [Large Language Model Programs](https://arxiv.org/abs/2305.05364) *— 2023 · arXiv:2305.05364*
58. [Large Language Models Can Self-Improve](https://aclanthology.org/2023.emnlp-main.67/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
59. [Learning to Reason and Memorize with Self-Notes](https://arxiv.org/abs/2305.00833) *— 2023 · arXiv:2305.00833*
60. [Let's Verify Step by Step](https://arxiv.org/abs/2305.20050) *— 2023 · arXiv:2305.20050*
61. [Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning](https://aclanthology.org/2023.findings-acl.668/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
62. [LogiCoT: Logical Chain-of-Thought Instruction Tuning](https://aclanthology.org/2023.findings-emnlp.191/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
63. [MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation](https://aclanthology.org/2023.findings-acl.704/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
64. [Making Language Models Better Reasoners with Step-Aware Verifier](https://aclanthology.org/2023.acl-long.291/) *— 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](https://arxiv.org/abs/2312.10730) *— 2023 · arXiv:2312.10730*
66. [MoT: Memory-of-Thought Enables ChatGPT to Self-Improve](https://aclanthology.org/2023.emnlp-main.392/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
67. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
68. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://arxiv.org/abs/2309.15402) *— 2023 · arXiv:2309.15402*
69. [Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://arxiv.org/abs/2212.10561) *— 2023 · arXiv:2212.10561*
70. [Pre-trained Language Models Can be Fully Zero-Shot Learners](https://aclanthology.org/2023.acl-long.869/) *— 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](https://arxiv.org/abs/2309.16797) *— 2023 · arXiv:2309.16797*
72. [Pushing the Limits of ChatGPT on NLP Tasks](https://arxiv.org/abs/2306.09719) *— 2023 · arXiv:2306.09719*
73. [Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency](https://arxiv.org/abs/2309.17382) *— 2023 · arXiv:2309.17382*
74. [Reinforced Self-Training (ReST) for Language Modeling](https://arxiv.org/abs/2308.08998) *— 2023 · arXiv:2308.08998*
75. [Rethinking with Retrieval: Faithful Large Language Model Inference](https://arxiv.org/abs/2301.00303) *— 2023 · arXiv:2301.00303*
76. [STEER: Unified Style Transfer with Expert Reinforcement](https://aclanthology.org/2023.findings-emnlp.506/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
77. [Scaling Relationship on Learning Mathematical Reasoning with Large Language Models](https://arxiv.org/abs/2308.01825) *— 2023 · arXiv:2308.01825*
78. [Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement](https://aclanthology.org/2023.findings-emnlp.762/) *— 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](https://aclanthology.org/2023.findings-emnlp.179/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
80. [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](https://arxiv.org/abs/2307.15337) *— 2023 · arXiv:2307.15337*
81. [Strategic Reasoning with Language Models](https://arxiv.org/abs/2305.19165) *— 2023 · arXiv:2305.19165*
82. [Symbolic Chain-of-Thought Distillation: Small Models Can Also ``Think'''' Step-by-Step](https://aclanthology.org/2023.acl-long.150/) *— 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](https://aclanthology.org/2023.findings-emnlp.946/) *— 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](https://aclanthology.org/2023.emnlp-main.782/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
85. [Think before you speak: Training Language Models With Pause Tokens](https://arxiv.org/abs/2310.02226) *— 2023 · arXiv:2310.02226*
86. [ThinkSum: Probabilistic reasoning over sets using large language models](https://aclanthology.org/2023.acl-long.68/) *— 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](https://arxiv.org/abs/2308.07902) *— 2023 · arXiv:2308.07902*
88. [ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving](https://arxiv.org/abs/2309.17452) *— 2023 · arXiv:2309.17452*
89. [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/abs/2302.04761) *— 2023 · arXiv:2302.04761*
90. [Towards Reasoning in Large Language Models: A Survey](https://aclanthology.org/2023.findings-acl.67/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
91. [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://arxiv.org/abs/2303.04671) *— 2023 · arXiv:2303.04671*
92. [Why think step by step? Reasoning emerges from the locality of experience](https://arxiv.org/abs/2304.03843) *— 2023 · arXiv:2304.03843*
93. [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://arxiv.org/abs/2308.09583) *— 2023 · arXiv:2308.09583*
94. [ZARA: Improving Few-Shot Self-Rationalization for Small Language Models](https://aclanthology.org/2023.findings-emnlp.310/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
95. [Can language models learn from explanations in context?](https://aclanthology.org/2022.findings-emnlp.38/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
96. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) *— 2022 · arXiv:2201.11903*
97. [Compositional Semantic Parsing with Large Language Models](https://arxiv.org/abs/2209.15003) *— 2022 · arXiv:2209.15003*
98. [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://arxiv.org/abs/2212.14024) *— 2022 · arXiv:2212.14024*
99. [Does Self-Rationalization Improve Robustness to Spurious Correlations?](https://aclanthology.org/2022.emnlp-main.501/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
100. [Explanations from Large Language Models Make Small Reasoners Better](https://arxiv.org/abs/2210.06726) *— 2022 · arXiv:2210.06726*

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