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Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their strong instruction-following abilities.
Rank analysis of incomplete block designs: I. the method of paired comparisons
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High-dimensional continuous control using generalized advantage estimation
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Deep reinforcement learning from human preferences
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Reinforcement learning for bandit neural machine translation with simulated human feedback
Khanh Nguyen, Hal Daumé III, and Jordan Boyd-Graber · 2017
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher · 2017
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Surreal: Open-source reinforcement learning framework and robot manipulation benchmark
Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, and Li Fei-Fei · 2018
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Unity: A general platform for intelligent agents
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Can neural machine translation be improved with user feedback?
Julia Kreutzer, Shahram Khadivi, Evgeny Matusov, and Stefan Riezler · 2018
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A reinforcement learning approach to interactive-predictive neural machine translation
Tsz Kin Lam, Julia Kreutzer, and Stefan Riezler · 2018
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Learning from dialogue after deployment: Feed yourself, chatbot!
Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazare, and Jason Weston · 2019
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CTRL: A Conditional Transformer Language Model for Controllable Generation
N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong, and R. Socher · 2019
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Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Learning to summarize from human feedback, 2020
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano · 2020
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Lyceum: An efficient and scalable ecosystem for robot learning
Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, and Emanuel Todorov · 2020
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Ext5: Towards extreme multi-task scaling for transfer learning
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q Tran, Dara Bahri, Jianmo Ni, et al · 2021
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A general language assistant as a laboratory for alignment, 2021
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, and Jared Kaplan · 2021
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On the opportunities and risks of foundation models
R. Bommasani et al · 2021
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Brax–a differentiable physics engine for large scale rigid body simulation
C Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem · 2021
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Offline rl for natural language generation with implicit language q learning
Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, and Sergey Levine · 2022
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Solving math word problems with process- and outcome-based feedback, 2022
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
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Self-instruct: Aligning language model with self generated instructions, 2022
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, et al · 2022
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Improving code generation by training with natural language feedback
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Samuel Kiegeland and Julia Kreutzer · 2021
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
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Turingbench: A benchmark environment for turing test in the age of neural text generation
Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Using large language models to simulate multiple humans
Gati Aher, Rosa I Arriaga, and Adam Tauman Kalai · 2022
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Out of one, many: Using language models to simulate human samples
Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, and David Wingate · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
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Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R Bowman, Kyunghyun Cho, and Ethan Perez · 2023
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Can large language models be an alternative to human evaluations?
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Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Koala: A dialogue model for academic research, March 2023
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
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Pretraining language models with human preferences
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez · 2023
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Aligning text-to-image models using human feedback
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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
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Chain of hindsight aligns language models with feedback
H Liu, C Sferrazza, and P Abbeel · 2023
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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G-eval: Nlg evaluation using gpt-4 with better human alignmentg
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The flan collection: Designing data and methods for effective instruction tuning
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Self-refine: Iterative refinement with self-feedback
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Gpt-4 technical report, 2023
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Generative agents: Interactive simulacra of human behavior
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
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Training language models with language feedback at scale
Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez · 2023
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Alpaca: A strong, replicable instruction-following modely, March 2023
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Llama: Open and efficient foundation language models
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Judging llm-as-a-judge with mt-bench and chatbot arena
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