🔗 Who cites «Supervising strong learners by amplifying weak experts»

39 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · ← back to «Supervising strong learners by amplifying weak experts»

1. Training language models to follow instructions with human feedbackarXiv:2203.02155Long Ouyang, Jeff Wu, Xu Jiang, et al.2022 · 10,224 citations
2. Constitutional AI: Harmlessness from AI FeedbackarXiv:2212.08073Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.2022 · 1,028 citations
3. WebGPT: Browser-assisted question-answering with human feedbackarXiv:2112.09332Reiichiro Nakano, Jacob Hilton, Suchir Balaji, et al.2021 · 721 citations
4. Improving alignment of dialogue agents via targeted human judgementsarXiv:2209.14375Amelia Glaese, Nat McAleese, Maja Trębacz, et al.2022 · 362 citations
5. Solving math word problems with process- and outcome-based feedbackarXiv:2211.14275Jonathan Uesato, Nate Kushman, Ramana Kumar, et al.2022 · 355 citations
6. Discovering Language Model Behaviors with Model-Written EvaluationsarXiv:2212.09251Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.2022 · 328 citations
7. RAFT: Reward rAnked FineTuning for Generative Foundation Model AlignmentarXiv:2304.06767Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.2023 · 252 citations
8. Scalable agent alignment via reward modeling: a research directionarXiv:1811.07871Jan Leike, David Krueger, Tom Everitt, et al.2018 · 227 citations
9. Unsupervised Question Decomposition for Question AnsweringConference on Empirical Methods in Natural Language Processing (EMNLP)Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.2020 · 222 citations
10. Teaching language models to support answers with verified quotesarXiv:2203.11147Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.2022 · 218 citations
11. Recursively Summarizing Books with Human FeedbackarXiv:2109.10862Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.2021 · 208 citations
12. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
13. GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via CipherarXiv:2308.06463Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.2023 · 168 citations
14. TALM: Tool Augmented Language ModelsarXiv:2205.12255Aaron Parisi, Yao Zhao, Noah Fiedel2022 · 149 citations
15. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
16. The Alignment Problem from a Deep Learning PerspectivearXiv:2209.00626Richard Ngo, Lawrence Chan, Soren Mindermann2022 · 121 citations
17. Alignment of Language AgentsarXiv:2103.14659Zachary Kenton, Tom Everitt, Laura Weidinger, et al.2021 · 120 citations
18. Measuring Progress on Scalable Oversight for Large Language ModelsarXiv:2211.03540Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, et al.2022 · 117 citations
19. AI safety via debatearXiv:1805.00899Geoffrey Irving, Paul Christiano, Dario Amodei2018 · 110 citations
20. Debating with More Persuasive LLMs Leads to More Truthful AnswersarXiv:2402.06782Akbir Khan, John Hughes, Dan Valentine, et al.2024 · 101 citations
21. A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
22. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
23. BOND: Aligning LLMs with Best-of-N DistillationarXiv:2407.14622Pier Giuseppe Sessa, Robert Dadashi, Leonard Hussenot, et al.2024 · 23 citations
24. Tandem Training for Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Robert West, Ashton Anderson, Ece Kamar, et al.2026

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

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/christiano-2018-supervising-strong-learners-amplifying-weak/cited-by/