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We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models.
Neural machine translation by jointly learning to align and translate
Yoshua Bengio Dzmitry Bahdanau, Kyunghyun Cho · 2015
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Openai charter, 2018
OpenAI · 2018
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Supervising strong learners by amplifying weak experts
Paul Christiano, Buck Shlegeris, and Dario Amodei · 2018
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
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Geoffrey Irving, Paul Christiano, and Dario Amodei · 2018
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Introducing chatgpt, 2022
OpenAI · 2022
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Agi, 2022
Gary Marcus · 2022
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Measuring progress on scalable oversight for large language models
Samuel R Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamilė Lukošiūtė, Amanda Askell, Andy Jones, Anna Chen, et al · 2022
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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
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Defining and characterizing reward gaming
Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger · 2022
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Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
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Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman · 2022
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Model evaluation for extreme risks
Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, Mary Phuong, Jess Whittlestone, Jade Leung, Daniel Kokotajlo, Nahema Marchal, Markus Anderljung, Noam Kolt, et al · 2023
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Large language model alignment: A survey
Tianhao Shen, Renren Jin, Yufei Huang, Chuang Liu, Weilong Dong, Zishan Guo, Xinwei Wu, Yan Liu, and Deyi Xiong · 2023
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Introducing superalignment, 2023
OpenAI · 2023
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Statistical rejection sampling improves preference optimization
Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J Liu, and Jialu Liu · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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Unveiling the implicit toxicity in large language models
Jiaxin Wen, Pei Ke, Hao Sun, Zhexin Zhang, Chengfei Li, Jinfeng Bai, and Minlie Huang · 2023
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Reinforcement learning for generative ai: A survey
Yuanjiang Cao, Quan Z Sheng, Julian McAuley, and Lina Yao · 2023
Generative verifiers: Reward modeling as next-token prediction
Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal · 2024
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AutoDetect: Towards a unified framework for automated weakness detection in large language models
Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, and Minlie Huang · 2024
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Language models learn to mislead humans via rlhf
Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R Bowman, He He, and Shi Feng · 2024
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Spar: Self-play with tree-search refinement
Jiale Cheng, Xiao Liu, Cunxiang Wang, Xiaotao Gu, Yida Lu, Dan Zhang, Yuxiao Dong, Jie Tang, Hongning Wang, and Minlie Huang · 2024
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A comprehensive study on robustness of image classification models: Benchmarking and rethinking
Chang Liu, Yinpeng Dong, Wenzhao Xiang, Xiao Yang, Hang Su, Jun Zhu, Yuefeng Chen, Yuan He, Hui Xue, and Shibao Zheng · 2024
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Judgelm: Fine-tuned large language models are scalable judges
Lianghui Zhu, Xinggang Wang, and Xinlong Wang · 2023
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Lm vs lm: Detecting factual errors via cross examination
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson · 2023
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Self-consuming generative models go mad, 2023
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard G. Baraniuk · 2023
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Human intelligence, 2024
Yann LeCun · 2024
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Ami (advanced machine intelligence), 2024
Yann LeCun · 2024
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Managing extreme ai risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, et al · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Adversarial attacks and defenses on text-to-image diffusion models: A survey
Chenyu Zhang, Mingwang Hu, Wenhui Li, and Lanjun Wang · 2024
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Improving reward models with synthetic critiques
Zihuiwen Ye, Fraser Greenlee-Scott, Max Bartolo, Phil Blunsom, Jon Ander Campos, and Matthias Gallé · 2024
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Learning to refine with fine-grained natural language feedback
Manya Wadhwa, Xinyu Zhao, Junyi Jessy Li, and Greg Durrett · 2024
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Improving model factuality with fine-grained critique-based evaluator
Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, et al · 2024
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LLM evaluators recognize and favor their own generations
Arjun Panickssery, Samuel R. Bowman, and Shi Feng · 2024
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Bayesian calibration of win rate estimation with LLM evaluators
Yicheng Gao, Gonghan Xu, Zhe Wang, and Arman Cohan · 2024
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Trust or escalate: Llm judges with provable guarantees for human agreement
Jaehun Jung, Faeze Brahman, and Yejin Choi · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
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Benchmarking foundation models with language-model-as-an-examiner
Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, et al · 2024
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The curse of recursion: Training on generated data makes models forget, 2024
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson · 2024
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The curious decline of linguistic diversity: Training language models on synthetic text, 2024
Yanzhu Guo, Guokan Shang, Michalis Vazirgiannis, and Chloé Clavel · 2024
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Large language models suffer from their own output: An analysis of the self-consuming training loop, 2024
Martin Briesch, Dominik Sobania, and Franz Rothlauf · 2024
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Progress or regress? self-improvement reversal in post-training, 2024
Ting Wu, Xuefeng Li, and Pengfei Liu · 2024
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Mind the gap: Examining the self-improvement capabilities of large language models, 2024
Yuda Song, Hanlin Zhang, Carson Eisenach, Sham Kakade, Dean Foster, and Udaya Ghai · 2024
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Panacea: Pareto alignment via preference adaptation for llms
Yifan Zhong, Chengdong Ma, Xiaoyuan Zhang, Ziran Yang, Haojun Chen, Qingfu Zhang, Siyuan Qi, and Yaodong Yang · 2024
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