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As large language models (LLMs) continue to evolve, ensuring their alignment with human goals and values remains a pressing challenge.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Risks from learned optimization in advanced machine learning systems
Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, and Scott Garrabrant. 2019 · 1906
Earlier work this paper cites.
The superintelligent will: Motivation and instrumental rationality in advanced artificial agents
Nick Bostrom. 2012 · 2012
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An overview of 11 proposals for building safe advanced ai
Evan Hubinger. 2020 · 2012
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016 · 2016
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Formalizing convergent instrumental goals
Tsvi Benson-Tilsen and Nate Soares. 2016 · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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The basic ai drives
Stephen M Omohundro. 2018 · 2018
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Human-aligned artificial intelligence is a multiobjective problem
Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin, and Jane Mummery. 2018 · 2018
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Human compatible: AI and the problem of control
Stuart Russell. 2019 · 2019
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Ethical issues in advanced artificial intelligence
Nick Bostrom. 2020 · 2020
Cited alongside, same era.
Artificial intelligence, values, and alignment
Iason Gabriel. 2020 · 2020
Cited alongside, same era.
A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al. 2021 · 2021
Cited alongside, same era.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
Cited alongside, same era.
Sgkd: A scalable and effective knowledge distillation framework for graph representation learning
Yufei He and Yao Ma. 2022 · 2022
Cited alongside, same era.
Trustworthy llms: A survey and guideline for evaluating large language models’ alignment
Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li. 2023 · 2023
Later among the works it cites.
Mle-bench: Evaluating machine learning agents on machine learning engineering
Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, et al. 2024 · 2024
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Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. 2024 · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. 2024 · 2024
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Graphmae2: A decoding-enhanced masked self-supervised graph learner
Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, and Jie Tang. 2023 · 2023
Cited alongside, same era.
Ai alignment: A comprehensive survey
Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, et al. 2023 · 2023
Cited alongside, same era.
Yufei He, Zhenyu Hou, Yukuo Cen, Feng He, Xu Cheng, and Bryan Hooi. 2024a
Cited in the paper.
Unigraph: Learning a unified cross-domain foundation model for text-attributed graphs
Yufei He, Yuan Sui, Xiaoxin He, and Bryan Hooi. 2024b
Cited in the paper.
Yuan Sui, Yufei He, Zifeng Ding, and Bryan Hooi. 2024a
Cited in the paper.
Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. 2024 · 2024
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Frontier models are capable of in-context scheming
Alexander Meinke, Bronson Schoen, Jérémy Scheurer, Mikita Balesni, Rusheb Shah, and Marius Hobbhahn. 2024 · 2024
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2024 · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. 2025 · 2025
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Unigraph2: Learning a unified embedding space to bind multimodal graphs
Yufei He, Yuan Sui, Xiaoxin He, Yue Liu, Yifei Sun, and Bryan Hooi. 2025 · 2025
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