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Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model.
A study of bfloat16 for deep learning training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, et al. 2019 · 1905
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Adaptive regularization of labels
Qianggang Ding, Sifan Wu, Hao Sun, Jiadong Guo, and Shu-Tao Xia. 2019 · 1908
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Adam: A method for stochastic optimization
Diederik P Kingma. 2014 · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton. 2015 · 2015
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Distilling knowledge from ensembles of neural networks for speech recognition
Yevgen Chebotar and Austin Waters. 2016 · 2016
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Overview of the nlpcc-iccpol 2016 shared task: Open domain chinese question answering
Nan Duan. 2016 · 2016
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Sequence-level knowledge distillation
Yoon Kim and Alexander M Rush. 2016 · 2016
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Overview of the nlpcc 2017 shared task: Open domain chinese question answering
Nan Duan and Duyu Tang. 2018 · 2017
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Efficient knowledge distillation from an ensemble of teachers
Takashi Fukuda, Masayuki Suzuki, Gakuto Kurata, Samuel Thomas, Jia Cui, and Bhuvana Ramabhadran. 2017 · 2017
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Transferring knowledge to smaller network with class-distance loss
Seungwook Kim and Hyo-Eun Kim. 2017 · 2017
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Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer
Nikos Komodakis and Sergey Zagoruyko. 2017 · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim. 2017 · 2017
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Variational information distillation for knowledge transfer
Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. 2019 · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew Mccallum. 2019 · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2020 · 2020
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. 2020 · 2020
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Distilling knowledge learned in bert for text generation
Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, and Jingjing Liu. 2020 · 2020
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Dynabert: Dynamic bert with adaptive width and depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu. 2020 · 2020
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Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2020 · 2020
Cited alongside, same era.
Fastbert: a self-distilling bert with adaptive inference time
Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, and Qi Ju. 2020 · 2020
Cited alongside, same era.
Improved knowledge distillation via teacher assistant
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh. 2020 · 2020
Cited alongside, same era.
Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020 · 2020
Cited alongside, same era.
Exclusivity-consistency regularized knowledge distillation for face recognition
Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality
Vicuna. 2023 · 2023
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Wizardlm: Empowering large language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023 · 2023
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Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al. 2024 · 2024
Closest in time.
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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Language modeling is compression
Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, et al. 2024 · 2024
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Xiaobo Wang, Tianyu Fu, Shengcai Liao, Shuo Wang, Zhen Lei, and Tao Mei. 2020b · 2020
Cited alongside, same era.
Bert-of-theseus: Compressing bert by progressive module replacing
Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, and Ming Zhou. 2020 · 2020
Cited alongside, same era.
Ternarybert: Distillation-aware ultra-low bit bert
Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, and Qun Liu. 2020 · 2020
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021 · 2021
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation
Taehyeon Kim, Jaehoon Oh, Nak Yil Kim, Sangwook Cho, and Se-Young Yun. 2021 · 2021
Cited alongside, same era.
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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Chatglm: A family of large language models from glm-130b to glm-4 all tools
Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Diego Rojas, Guanyu Feng, Hanlin Zhao, Hanyu Lai, et al. 2024 · 2024
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Minillm: Knowledge distillation of large language models
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2024 · 2024
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Apple intelligence foundation language models
Tom Gunter, Zirui Wang, Chong Wang, Ruoming Pang, Andy Narayanan, Aonan Zhang, Bowen Zhang, Chen Chen, Chung-Cheng Chiu, David Qiu, et al. 2024 · 2024
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Minicpm: Unveiling the potential of small language models with scalable training strategies
Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, et al. 2024 · 2024
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C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models
Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Yao Fu, et al. 2024 · 2024
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Justin Kiefel and Shrey Shah. 2024 · 2024
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Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model
Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, et al. 2024 · 2024
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Compact language models via pruning and knowledge distillation
Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, and Pavlo Molchanov. 2024 · 2024
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Adelie: Aligning large language models on information extraction
Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, and Juanzi Li. 2024 · 2024
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Conifer: Improving complex constrained instruction-following ability of large language models
Haoran Sun, Lixin Liu, Junjie Li, Fengyu Wang, Baohua Dong, Ran Lin, and Ruohui Huang. 2024 · 2024
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Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024 · 2024
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Qwen2.5: A party of foundation models
Qwen Team. 2024 · 2024
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Llm pruning and distillation in practice: The minitron approach
Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, and Pavlo Molchanov. 2024 · 2024
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Dynamic temperature knowledge distillation
Yukang Wei and Yu Bai. 2024 · 2024
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A survey on knowledge distillation of large language models
Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou. 2024 · 2024
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Knowledge distillation based on transformed teacher matching
Kaixiang Zheng and EN-HUI YANG. 2024 · 2024
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