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The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Reward learning from human preferences and demonstrations in atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, and Dario Amodei · 2018
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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard · 2019
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Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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How fine can fine-tuning be? learning efficient language models
Evani Radiya-Dixit and Xin Wang · 2020
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Recipes for safety in open-domain chatbots
Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, and Emily Dinan · 2020
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Multisiam: Self-supervised multi-instance siamese representation learning for autonomous driving
Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, and Dit-Yan Yeung · 2021
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Soda10m: Towards large-scale object detection benchmark for autonomous driving
Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Xiaodan Liang, and Chunjing Xu · 2021
Earlier work this paper cites.
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
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Towards understanding and mitigating social biases in language models
Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2021
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Gpt3-to-plan: Extracting plans from text using gpt-3
Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2021
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Bbq: A hand-built bias benchmark for question answering
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R Bowman · 2021
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Safetykit: First aid for measuring safety in open-domain conversational systems
Emily Dinan, Gavin Abercrombie, Stevie A Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, Verena Rieser, et al · 2022
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Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang · 2022
Earlier work this paper cites.
Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar · 2022
Cited alongside, same era.
Is synthetic data from generative models ready for image recognition?
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi · 2022
Cited alongside, same era.
Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han · 2022
Cited alongside, same era.
Coda: A real-world road corner case dataset for object detection in autonomous driving
Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei Zhang, Chunjing Xu, Dit-Yan Yeung, et al · 2022
Cited alongside, same era.
Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi · 2023
Closest in time.
Trackdiffusion: Multi-object tracking data generation via diffusion models
Pengxiang Li, Zhili Liu, Kai Chen, Lanqing Hong, Yunzhi Zhuge, Dit-Yan Yeung, Huchuan Lu, and Xu Jia · 2023
Closest in time.
Self: Language-driven self-evolution for large language model
Jianqiao Lu, Wanjun Zhong, Wenyong Huang, Yufei Wang, Fei Mi, Baojun Wang, Weichao Wang, Lifeng Shang, and Qun Liu · 2023
Closest in time.
GPT-4 Technical Report, 2023
OpenAI · 2023
Closest in time.
Direct preference optimization: Your language model is secretly a reward model
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Task-customized self-supervised pre-training with scalable dynamic routing
Zhili Liu, Jianhua Han, Kai Chen, Lanqing Hong, Hang Xu, Chunjing Xu, and Zhenguo Li · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Constructing highly inductive contexts for dialogue safety through controllable reverse generation
Zhexin Zhang, Jiale Cheng, Hao Sun, Jiawen Deng, Fei Mi, Yasheng Wang, Lifeng Shang, and Minlie Huang · 2022
Cited alongside, same era.
Pku-beaver: Constrained value-aligned llm via safe rlhf
Juntao Dai, Xuehai Pan, Jiaming Ji, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2023
Cited alongside, same era.
Raft: Reward ranked finetuning for generative foundation model alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang · 2023
Cited alongside, same era.
Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
Cited alongside, same era.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
Closest in time.
Alpaca: An instruction-following llama model, 2023
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
Closest in time.
Stablerep: Synthetic images from text-to-image models make strong visual representation learners
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, and Dilip Krishnan · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Closest in time.
Aligning large language models with human: A survey
Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu · 2023
Closest in time.
Rlcd: Reinforcement learning from contrast distillation for language model alignment
Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, and Yuandong Tian · 2023
Closest in time.
A comprehensive capability analysis of gpt-3 and gpt-3.5 series models
Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang Shen, et al · 2023
Closest in time.
Rrhf: Rank responses to align language models with human feedback without tears
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang · 2023
Closest in time.
GLM-130b: An open bilingual pre-trained model
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang · 2023
Closest in time.
Task-customized masked autoencoder via mixture of cluster-conditional experts
LIU Zhili, Kai Chen, Jianhua Han, HONG Lanqing, Hang Xu, Zhenguo Li, and James Kwok · 2023
Closest in time.