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Who cites Chulhee Yun

70 citations of their works from 69 papers of our library (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · ← Chulhee Yun

  1. RoFormer: Enhanced Transformer with Rotary Position EmbeddingarXiv:2104.09864Jianlin Su, Yu Lu, Shengfeng Pan, et al.2021 · 3,028 citations
  2. Big Bird: Transformers for Longer SequencesConference on Neural Information Processing Systems (NeurIPS)Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.2020 · 1,023 citations
  3. What learning algorithm is in-context learning? Investigations with linear modelsarXiv:2211.15661Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.2022 · 236 citations
  4. What Can Transformers Learn In-Context? A Case Study of Simple Function ClassesConference on Neural Information Processing Systems (NeurIPS)Shivam Garg, Dimitris Tsipras, Percy Liang, et al.2022 · 209 citations
  5. On the Ability and Limitations of Transformers to Recognize Formal LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP)Satwik Bhattamishra, Kabir Ahuja, Navin Goyal2020 · 113 citations
  6. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
  7. Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit ComplexityTransactions of the Association for Computational Linguistics (TACL)Yiding Hao, Dana Angluin, Robert Frank2022 · 62 citations
  8. Teaching Arithmetic to Small TransformersarXiv:2307.03381Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.2023 · 57 citations
  9. Self-Attention Networks Can Process Bounded Hierarchical LanguagesarXiv:2105.11115Shunyu Yao, Binghui Peng, Christos Papadimitriou, et al.2021 · 43 citations
  10. Transformers learn to implement preconditioned gradient descent for in-context learningConference on Neural Information Processing Systems (NeurIPS)Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, et al.2023 · 37 citations
  11. Understanding Transformer Reasoning Capabilities via Graph AlgorithmsConference on Neural Information Processing Systems (NeurIPS)Clayton Sanford, Bahare Fatemi, Ethan Hall, et al.2024 · 21 citations
  12. Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic TasksarXiv:2406.01895Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.2024 · 5 citations
  13. Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute ScalingFindings of the Association for Computational LinguisticsIvan Rodkin, Daniil Orel, Konstantin Smirnov, et al.2026
  14. Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting CircuitsFindings of the Association for Computational LinguisticsYizheng Zhao2026
  15. Language Acquisition Device in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Masato Mita, Taiga Someya, Ryo Yoshida, et al.2026
  16. SCOPE: Boosting LLM Efficiency with Scoped Position EncodingAnnual Meeting of the Association for Computational Linguistics (ACL)Qingguo Qi, Hongyang Chen, Zhao Li2026
  17. Understanding the Prompt SensitivityAnnual Meeting of the Association for Computational Linguistics (ACL)Yang Liu, Chenhui Chu2026

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