🔗 Who cites «Unsolved Problems in ML Safety»

66 papers of our library cite it (of the 149,439 whose bibliographies we read) · 26 with a page here, the most cited first · ← back to «Unsolved Problems in ML Safety»

1. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
2. Emergent Abilities of Large Language ModelsarXiv:2206.07682Jason Wei, Yi Tay, Rishi Bommasani, et al.2022 · 2,269 citations
3. Red Teaming Language Models with Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Ethan Perez, Saffron Huang, Francis Song, et al.2022 · 700 citations
4. Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language GenerationarXiv:2302.09664Lorenz Kuhn, Yarin Gal, Sebastian Farquhar2023 · 433 citations
5. Representation Engineering: A Top-Down Approach to AI TransparencyarXiv:2310.01405Andy Zou, Long Phan, Sarah Chen, et al.2023 · 397 citations
6. Tool Learning with Foundation ModelsACM Computing SurveysYujia Qin, Shengding Hu, Yankai Lin, et al.2023 · 383 citations
7. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
8. Predictability and Surprise in Large Generative ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Deep Ganguli, Danny Hernandez, Liane Lovitt, et al.2022 · 268 citations
9. BlenderBot 3: a deployed conversational agent that continually learns to responsibly engagearXiv:2208.03188Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.2022 · 241 citations
10. Baseline Defenses for Adversarial Attacks Against Aligned Language ModelsarXiv:2309.00614Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.2023 · 236 citations
11. Prompting GPT-3 To Be ReliablearXiv:2210.09150Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.2022 · 210 citations
12. Generative Representational Instruction TuningarXiv:2402.09906Niklas Muennighoff, Hongjin Su, Liang Wang, et al.2024 · 172 citations
13. Detecting Language Model Attacks with PerplexityarXiv:2308.14132Gabriel Alon, Michael Kamfonas2023 · 141 citations
14. Evaluating the Moral Beliefs Encoded in LLMsConference on Neural Information Processing Systems (NeurIPS)Nino Scherrer, Claudia Shi, Amir Feder, et al.2023 · 129 citations
15. The WMDP Benchmark: Measuring and Reducing Malicious Use With UnlearningarXiv:2403.03218Nathaniel Li, Alexander Pan, Anjali Gopal, et al.2024 · 127 citations
16. LM vs LM: Detecting Factual Errors via Cross ExaminationConference on Empirical Methods in Natural Language Processing (EMNLP)Roi Cohen, May Hamri, Mor Geva, et al.2023 · 126 citations
17. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
18. The Alignment Problem from a Deep Learning PerspectivearXiv:2209.00626Richard Ngo, Lawrence Chan, Soren Mindermann2022 · 121 citations
19. Fundamental Limitations of Alignment in Large Language ModelsarXiv:2304.11082Yotam Wolf, Noam Wies, Oshri Avnery, et al.2023 · 114 citations
20. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
21. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
22. Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red TeamingarXiv:2501.18837Mrinank Sharma, Meg Tong, Jesse Mu, et al.2025 · 29 citations
23. Language Model Alignment in Multilingual Trolley ProblemsarXiv:2407.02273Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, et al.2024 · 23 citations
24. Do Large Language Models Reflect Demographic Pluralism in Safety?Findings of the Association for Computational LinguisticsUsman Naseem, Gautam Siddharth Kashyap, Sushant Kumar Ray, et al.2026 · 3 citations
25. SteuerLLM: Local specialized large language model for German tax law analysisarXiv:2602.11081 · Scientific Reports, Volume 16, 2026Sebastian Wind, Jeta Sopa, Laurin Schmid, et al.2026
26. Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Pankayaraj Pathmanathan, Furong Huang2026

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

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