🔗 Who cites «Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions»

21 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions»

1. Explainability for Large Language Models: A SurveyACM Transactions on Intelligent Systems and TechnologyHaiyan Zhao, Hanjie Chen, Fan Yang, et al.2023 · 790 citations
2. Unleashing the potential of prompt engineering for large language modelsarXiv:2310.14735 · Patterns 6(6) 101,260 (2025)Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.2023 · 536 citations
3. The Impact of Reasoning Step Length on Large Language ModelsFindings of the Association for Computational LinguisticsMingyu Jin, Qinkai Yu, Dong Shu, et al.2024 · 119 citations
4. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language ModelsFindings of the Association for Computational LinguisticsHengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.2026 · 9 citations
5. Do LLMs Encode Functional Importance of Reasoning Tokens ?Annual Meeting of the Association for Computational Linguistics (ACL)Janvijay Singh, Dilek Hakkani-Tür2026 · 2 citations
6. How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and ActivationFindings of the Association for Computational LinguisticsHao Yang, Qinghua Zhao, Lei Li, et al.2026 · 1 citation
7. Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient OrganizationAnnual Meeting of the Association for Computational Linguistics (ACL)Jia Liu, Jiaxin Luo, Weiwen Xu, et al.2026 · 1 citation

14 more papers of our library cite it; they have no page here yet.

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