Elamur Research⌕
☰
home / authors / Monte MacDiarmid / cited by

Who cites Monte MacDiarmid

401 citations of their works from 367 papers of our library (of the 149,439 whose bibliographies we read) · 108 with a page here, the most cited first · page 1 of 2 · 1–100 · ← Monte MacDiarmid

1 2

  1. Representation Engineering: A Top-Down Approach to AI TransparencyarXiv:2310.01405Andy Zou, Long Phan, Sarah Chen, et al.2023 · 397 citations
  2. Refusal in Language Models Is Mediated by a Single DirectionarXiv:2406.11717Andy Arditi, Oscar Obeso, Aaquib Syed, et al.2024 · 196 citations
  3. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
  4. SOTOPIA: Interactive Evaluation for Social Intelligence in Language AgentsarXiv:2310.11667Xuhui Zhou, Hao Zhu, Leena Mathur, et al.2023 · 138 citations
  5. Function Vectors in Large Language ModelsarXiv:2310.15213Eric Todd, Millicent L. Li, Arnab Sen Sharma, et al.2023 · 127 citations
  6. The Alignment Problem from a Deep Learning PerspectivearXiv:2209.00626Richard Ngo, Lawrence Chan, Soren Mindermann2022 · 121 citations
  7. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
  8. Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2arXiv:2408.05147Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, et al.2024 · 115 citations
  9. Fundamental Limitations of Alignment in Large Language ModelsarXiv:2304.11082Yotam Wolf, Noam Wies, Oshri Avnery, et al.2023 · 114 citations
  10. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
  11. Reasoning Models Don't Always Say What They ThinkarXiv:2505.05410Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.2025 · 106 citations
  12. Debating with More Persuasive LLMs Leads to More Truthful AnswersarXiv:2402.06782Akbir Khan, John Hughes, Dan Valentine, et al.2024 · 101 citations
  13. In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space SteeringarXiv:2311.06668Sheng Liu, Haotian Ye, Lei Xing, et al.2023 · 89 citations
  14. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
  15. Monitoring Reasoning Models for Misbehavior and the Risks of Promoting ObfuscationarXiv:2503.11926Bowen Baker, Joost Huizinga, Leo Gao, et al.2025 · 63 citations
  16. Arithmetic Without Algorithms: Language Models Solve Math With a Bag of HeuristicsarXiv:2410.21272Yaniv Nikankin, Anja Reusch, Aaron Mueller, et al.2024 · 51 citations
  17. Introducing v0.5 of the AI Safety Benchmark from MLCommonsarXiv:2404.12241Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.2024 · 38 citations
  18. Personality Alignment of Large Language ModelsarXiv:2408.11779Minjun Zhu, Yixuan Weng, Linyi Yang, et al.2024 · 21 citations
  19. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language ModelsarXiv:2503.24377Rui Wang, Hongru Wang, Boyang Xue, et al.2025 · 16 citations
  20. 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language ModelsarXiv:2505.00551Chong Zhang, Yue Deng, Xiang Lin, et al.2025 · 9 citations
  21. Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMsarXiv:2607.03936Kareem Elozeiri, Mervat Abassy, Omar Kallas, et al.2026 · 1 citation
  22. LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation SteeringarXiv:2607.06845Huan Wu, Ali Emami, Muhammad Furquan Hassan, et al.2026

1 2

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/monte-macdiarmid/cited-by/