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Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large language models (LLMs) in recent years.
Extensions of lipshitz mapping into hilbert space
William B Johnson. 1984 · 1984
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Adam: A method for stochastic optimization
Diederik P Kingma. 2014 · 2014
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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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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan. 2020 · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
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Calibrating sequence likelihood improves conditional language generation
Yao Zhao, Mikhail Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, and Peter J Liu. 2022 · 2022
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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Safe rlhf: Safe reinforcement learning from human feedback
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. 2023 · 2023
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Human-like summarization evaluation with chatgpt
Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan. 2023 · 2023
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Understanding in-context learning via supportive pretraining data
Xiaochuang Han, Daniel Simig, Todor Mihaylov, Yulia Tsvetkov, Asli Celikyilmaz, and Tianlu Wang. 2023 · 2023
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Alpacaeval: An automatic evaluator of instruction-following models
Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023 · 2023
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Wei Liu, Weihao Zeng, Keqing He, Yong Jiang, and Junxian He. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Instructiongpt-4: A 200-instruction paradigm for fine-tuning minigpt-4
Lai Wei, Zihao Jiang, Weiran Huang, and Lichao Sun. 2023 · 2023
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Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen. 2024 · 2024
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Filtered direct preference optimization
Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai, Kenshi Abe, and Kaito Air. 2024 · 2024
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Text clustering with llm embeddings
Alina Petukhova, Joao P Matos-Carvalho, and Nuno Fachada. 2024 · 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 · 2024
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Insights into alignment: Evaluating dpo and its variants across multiple tasks
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Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, et al. 2024 · 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 · 2024
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Ultrafeedback: Boosting language models with scaled ai feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, et al. 2024 · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
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Length-controlled alpacaeval: A simple way to debias automatic evaluators
Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B Hashimoto. 2024 · 2024
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Kto: Model alignment as prospect theoretic optimization
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. 2024 · 2024
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Calibrated language models must hallucinate
Adam Tauman Kalai and Santosh S. Vempala. 2024 · 2024
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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. 2024 · 2024
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Amir Saeidi, Shivanshu Verma, and Chitta Baral. 2024 · 2024
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Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2024 · 2024
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Large language models for data annotation: A survey
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Self-training with direct preference optimization improves chain-of-thought reasoning
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Less: Selecting influential data for targeted instruction tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen. 2024 · 2024
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Deliberate reasoning for llms as structure-aware planning with accurate world model
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Hengyuan Zhang, Yanru Wu, Dawei Li, Zacc Yang, Rui Zhao, Yong Jiang, and Fei Tan. 2024 · 2024
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Lima: Less is more for alignment
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