🔗 Who cites «Explanations from Large Language Models Make Small Reasoners Better»

88 papers of our library cite it (of the 149,439 whose bibliographies we read) · 30 with a page here, the most cited first · ← back to «Explanations from Large Language Models Make Small Reasoners Better»

1. Towards Reasoning in Large Language Models: A SurveyFindings of the Association for Computational LinguisticsJie Huang, Kevin Chen-Chuan Chang2023 · 780 citations
2. MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsarXiv:2309.12284Longhui Yu, Weisen Jiang, Han Shi, et al.2023 · 565 citations
3. A Survey on Model Compression for Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Xunyu Zhu, Jian Li, Yong Liu, et al.2024 · 381 citations
4. Teaching Small Language Models to ReasonAnnual Meeting of the Association for Computational Linguistics (ACL)Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, et al.2023 · 343 citations
5. On-Policy Distillation of Language Models: Learning from Self-Generated MistakesarXiv:2306.13649Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.2023 · 299 citations
6. Measuring Faithfulness in Chain-of-Thought ReasoningarXiv:2307.13702Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.2023 · 262 citations
7. Specializing Smaller Language Models towards Multi-Step ReasoningarXiv:2301.12726Yao Fu, Hao Peng, Litu Ou, et al.2023 · 224 citations
8. Distilling Reasoning Capabilities into Smaller Language ModelsFindings of the Association for Computational LinguisticsKumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan2023 · 219 citations
9. Reasoning with Language Model Prompting: A SurveyarXiv:2212.09597Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.2022 · 161 citations
10. SCOTT: Self-Consistent Chain-of-Thought DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Peifeng Wang, Zhengyang Wang, Zheng Li, et al.2023 · 146 citations
11. Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-StepAnnual Meeting of the Association for Computational Linguistics (ACL)Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.2023 · 140 citations
12. Quiet-STaR: Language models can teach themselves to think before speaking2024 · 122 citations
13. Efficient Large Language Models: A SurveyarXiv:2312.03863Zhongwei Wan, Xin Wang, Che Liu, et al.2023 · 110 citations
14. Multi-Step Reasoning with Large Language Models, a SurveyACM Computing SurveysAske Plaat, Annie Wong, Suzan Verberne, et al.2024 · 106 citations
15. On Learning to Summarize with Large Language Models as ReferencesConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Yixin Liu, Kejian Shi, Katherine He, et al.2024 · 94 citations
16. Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought ReasoningFindings of the Association for Computational LinguisticsDebjit Paul, Robert West, Antoine Bosselut, et al.2024 · 79 citations
17. Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive TasksConference on Neural Information Processing Systems (NeurIPS)Minki Kang, Seanie Lee, Jinheon Baek, et al.2023 · 71 citations
18. Learning From Mistakes Makes LLM Better ReasonerarXiv:2310.20689Shengnan An, Zexiong Ma, Zeqi Lin, et al.2023 · 64 citations
19. SatLM: Satisfiability-Aided Language Models Using Declarative PromptingConference on Neural Information Processing Systems (NeurIPS)Xi Ye, Qiaochu Chen, Isil Dillig, et al.2023 · 62 citations
20. The Life Cycle of Knowledge in Big Language Models: A SurveyarXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024Boxi Cao, Hongyu Lin, Xianpei Han, et al.2023 · 31 citations
21. What is the Role of Small Models in the LLM Era: A SurveyComputational LinguisticsLihu Chen, Gael Varoquaux2024 · 23 citations
22. EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error CorrectionProceedings of the AAAI Conference on Artificial IntelligenceJingheng Ye, Shang Qin, Yinghui Li, et al.2024 · 15 citations
23. QCRD: Quality-guided Contrastive Rationale Distillation for Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Wei Wang, Zhaowei Li, Qi Xu, et al.2025 · 13 citations
24. SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language ModelsInternational Conference on Computational Linguistics (COLING)Huanxuan Liao, Shizhu He, Yupu Hao, et al.2025 · 12 citations
25. A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLIFindings of the Association for Computational LinguisticsBeiduo Chen, Siyao Peng, Anna Korhonen, et al.2025 · 9 citations
26. Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-TuningInternational Conference on Computational Linguistics (COLING)Jing Bi, Yuting Wu, Weiwei Xing, et al.2025 · 8 citations
27. LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language InferenceConference on Empirical Methods in Natural Language Processing (EMNLP)Pingjun Hong, Beiduo Chen, Siyao Peng, et al.2025 · 8 citations
28. TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge DistillationarXiv:2503.04872Lin Sun, Guangxiang Zhao, Xiaoqi Jian, et al.2025 · 7 citations
29. Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank SubstitutionFindings of the Association for Computational LinguisticsYuhua Zhou, Shichao Weng, Changhai Zhou, et al.2026 · 1 citation
30. NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question AnsweringFindings of the Association for Computational LinguisticsVishal Pramanik, Maisha Maliha, Nathaniel D. Bastian, et al.2026

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