---
title: "Who cites Nayoung Lee — 81 papers of our library"
description: "81 papers of the Elamur Research library cite the works of Nayoung Lee, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/nayoung-lee/cited-by/"
---
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# 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](https://md.research.elamur.ai/free/en/authors/nayoung-lee/)

1. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
2. [**Jamba: A Hybrid Transformer-Mamba Language Model arXiv:2403.19887**Opher Lieber, Barak Lenz, Hofit Bata, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/lieber-2024-jamba-hybrid-transformer-mamba-language/)
3. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution arXiv:2401.03065**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/)
4. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/)
5. [**An Empirical Study of Mamba-based Language Models arXiv:2406.07887**Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.*2024 · 67 citations*](https://md.research.elamur.ai/free/en/papers/waleffe-2024-empirical-study-mamba-based-language/)
6. [**What Algorithms can Transformers Learn? A Study in Length Generalization arXiv:2310.16028**Hattie Zhou, Arwen Bradley, Etai Littwin, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/)
7. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
8. [**Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling arXiv:2406.07522**Liliang Ren, Yang Liu, Yadong Lu, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/ren-2024-samba-simple-hybrid-state-space/)
9. [**Premise Order Matters in Reasoning with Large Language Models arXiv:2402.08939**Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-premise-order-matters-reasoning-large/)
10. [**Repeat After Me: Transformers are Better than State Space Models at Copying arXiv:2402.01032**Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/)
11. [**Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs arXiv:2402.14903**Aaditya K. Singh, DJ Strouse*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-tokenization-counts-impact-tokenization-arithmetic/)
12. [**Hymba: A Hybrid-head Architecture for Small Language Models arXiv:2411.13676**Xin Dong, Yonggan Fu, Shizhe Diao, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-hymba-hybrid-head-architecture-small/)
13. [**Zamba: A Compact 7B SSM Hybrid Model arXiv:2405.16712**Paolo Glorioso, Quentin Anthony, Yury Tokpanov, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/glorioso-2024-zamba-compact-7b-ssm-hybrid/)
14. [**Reasoning with Latent Thoughts: On the Power of Looped Transformers arXiv:2502.17416**Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, et al.*2025 · 44 citations*](https://md.research.elamur.ai/free/en/papers/saunshi-2025-reasoning-latent-thoughts-power-looped/)
15. [**The pitfalls of next-token prediction arXiv:2403.06963**Gregor Bachmann, Vaishnavh Nagarajan*2024 · 39 citations*](https://md.research.elamur.ai/free/en/papers/bachmann-2024-pitfalls-next-token-prediction/)
16. **[Transformers Can Do Arithmetic with the Right Embeddings](https://md.research.elamur.ai/free/en/papers/mcleish-2024-transformers-arithmetic-right-embeddings/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sean McLeish, Arpit Bansal, Alex Stein, et al.*2024 · 36 citations*
17. **[The Hidden Attention of Mamba Models](https://md.research.elamur.ai/free/en/papers/ali-2025-hidden-attention-mamba-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ameen Ali Ali, Itamar Zimerman, Lior Wolf*2025 · 32 citations*
18. [**Positional Description Matters for Transformers Arithmetic arXiv:2311.14737**Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-positional-description-matters-transformers-arithmetic/)
19. [**Transformers Can Achieve Length Generalization But Not Robustly arXiv:2402.09371**Yongchao Zhou, Uri Alon, Xinyun Chen, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-transformers-achieve-length-generalization-but/)
20. **[NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/schwartz-2024-numerologic-number-encoding-enhanced-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eli Schwartz, Leshem Choshen, Joseph Shtok, et al.*2024 · 26 citations*
21. **[Understanding Transformer Reasoning Capabilities via Graph Algorithms](https://md.research.elamur.ai/free/en/papers/sanford-2024-understanding-transformer-reasoning-capabilities-graph/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Clayton Sanford, Bahare Fatemi, Ethan Hall, et al.*2024 · 21 citations*
22. [**RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval arXiv:2402.18510**Kaiyue Wen, Xingyu Dang, Kaifeng Lyu*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/wen-2024-rnns-transformers-yet-key-bottleneck/)
23. [**Looped Transformers for Length Generalization arXiv:2409.15647**Ying Fan, Yilun Du, Kannan Ramchandran, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/fan-2024-looped-transformers-length-generalization/)
24. **[Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles](https://md.research.elamur.ai/free/en/papers/shah-2024-causal-language-modeling-elicit-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kulin Shah, Nishanth Dikkala, Xin Wang, et al.*2024 · 12 citations*
25. [**Chain-of-Thought Reasoning is a Policy Improvement Operator arXiv:2309.08589**Hugh Zhang, David C. Parkes*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-chain-thought-reasoning-policy-improvement/)
26. [**Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks arXiv:2406.01895**Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/sabbaghi-2024-explicitly-encoding-structural-symmetry-key/)
27. **[Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth](https://md.research.elamur.ai/free/en/papers/yuan-2026-barriers-discrete-reasoning-transformers-survey/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/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 LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-implicit-explicit-token-efficient-logical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shaojie Wang, Liang Zhang*2026*
29. **[Task-Related In-Context Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-task-related-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenqiang Wang, Peng Chen, Yan Xiao, et al.*2026*
30. **[Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures](https://md.research.elamur.ai/free/en/papers/wang-2026-understanding-context-learning-beyond-transformers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shenran Wang, Timothy Tin-Long Tse, Jian Zhu*2026*
31. **[XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection](https://md.research.elamur.ai/free/en/papers/ng-2026-xlsr-mambo-scaling-hybrid-mamba/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kwok-Ho Ng, Tingting Song, Yongdong WU, et al.*2026*

50 more citing papers of our library have no page here yet.

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