🧑‍🔬 Who cites Nayoung Lee

81 citations of their works from 81 papers of our library (of the 149,439 whose bibliographies we read) · 31 with a page here, the most cited first · ← Nayoung Lee

1. 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
2. Jamba: A Hybrid Transformer-Mamba Language ModelarXiv:2403.19887Opher Lieber, Barak Lenz, Hofit Bata, et al.2024 · 159 citations
3. CRUXEval: A Benchmark for Code Reasoning, Understanding and ExecutionarXiv:2401.03065Alex Gu, Baptiste Rozière, Hugh Leather, et al.2024 · 122 citations
4. A Survey on Efficient Inference for Large Language ModelsarXiv:2404.14294Zixuan Zhou, Xuefei Ning, Ke Hong, et al.2024 · 113 citations
5. An Empirical Study of Mamba-based Language ModelsarXiv:2406.07887Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.2024 · 67 citations
6. What Algorithms can Transformers Learn? A Study in Length GeneralizationarXiv:2310.16028Hattie Zhou, Arwen Bradley, Etai Littwin, et al.2023 · 67 citations
7. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
8. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language ModelingarXiv:2406.07522Liliang Ren, Yang Liu, Yadong Lu, et al.2024 · 59 citations
9. Premise Order Matters in Reasoning with Large Language ModelsarXiv:2402.08939Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.2024 · 58 citations
10. Repeat After Me: Transformers are Better than State Space Models at CopyingarXiv:2402.01032Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.2024 · 57 citations
11. Tokenization counts: the impact of tokenization on arithmetic in frontier LLMsarXiv:2402.14903Aaditya K. Singh, DJ Strouse2024 · 55 citations
12. Hymba: A Hybrid-head Architecture for Small Language ModelsarXiv:2411.13676Xin Dong, Yonggan Fu, Shizhe Diao, et al.2024 · 50 citations
13. Zamba: A Compact 7B SSM Hybrid ModelarXiv:2405.16712Paolo Glorioso, Quentin Anthony, Yury Tokpanov, et al.2024 · 50 citations
14. Reasoning with Latent Thoughts: On the Power of Looped TransformersarXiv:2502.17416Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, et al.2025 · 44 citations
15. The pitfalls of next-token predictionarXiv:2403.06963Gregor Bachmann, Vaishnavh Nagarajan2024 · 39 citations
16. Transformers Can Do Arithmetic with the Right EmbeddingsConference on Neural Information Processing Systems (NeurIPS)Sean McLeish, Arpit Bansal, Alex Stein, et al.2024 · 36 citations
17. The Hidden Attention of Mamba ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ameen Ali Ali, Itamar Zimerman, Lior Wolf2025 · 32 citations
18. Positional Description Matters for Transformers ArithmeticarXiv:2311.14737Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, et al.2023 · 30 citations
19. Transformers Can Achieve Length Generalization But Not RobustlyarXiv:2402.09371Yongchao Zhou, Uri Alon, Xinyun Chen, et al.2024 · 29 citations
20. NumeroLogic: Number Encoding for Enhanced LLMs' Numerical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Eli Schwartz, Leshem Choshen, Joseph Shtok, et al.2024 · 26 citations
21. Understanding Transformer Reasoning Capabilities via Graph AlgorithmsConference on Neural Information Processing Systems (NeurIPS)Clayton Sanford, Bahare Fatemi, Ethan Hall, et al.2024 · 21 citations
22. RNNs are not Transformers (Yet): The Key Bottleneck on In-context RetrievalarXiv:2402.18510Kaiyue Wen, Xingyu Dang, Kaifeng Lyu2024 · 17 citations
23. Looped Transformers for Length GeneralizationarXiv:2409.15647Ying Fan, Yilun Du, Kannan Ramchandran, et al.2024 · 13 citations
24. Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic PuzzlesConference on Neural Information Processing Systems (NeurIPS)Kulin Shah, Nishanth Dikkala, Xin Wang, et al.2024 · 12 citations
25. Chain-of-Thought Reasoning is a Policy Improvement OperatorarXiv:2309.08589Hugh Zhang, David C. Parkes2023 · 11 citations
26. Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic TasksarXiv:2406.01895Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.2024 · 5 citations
27. Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and BandwidthConference of the European Chapter of the Association for Computational Linguistics (EACL)Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, et al.2026 · 3 citations
28. From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMsFindings of the Association for Computational LinguisticsShaojie Wang, Liang Zhang2026
29. Task-Related In-Context LearningFindings of the Association for Computational LinguisticsWenqiang Wang, Peng Chen, Yan Xiao, et al.2026
30. Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid ArchitecturesFindings of the Association for Computational LinguisticsShenran Wang, Timothy Tin-Long Tse, Jian Zhu2026
31. XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake DetectionFindings of the Association for Computational LinguisticsKwok-Ho Ng, Tingting Song, Yongdong WU, et al.2026

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