Fetching the paper…
Reading the bibliography…
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models through knowledge distillation (KD) from these powerful yet black-box teachers.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
On the efficacy of knowledge distillation
Jang Hyun Cho and Bharath Hariharan. 2019 · 2019
Earlier work this paper cites.
Patient knowledge distillation for BERT model compression
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019 · 2019
Earlier work this paper cites.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Improved knowledge distillation via teacher assistant
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh. 2020 · 2020
Earlier work this paper cites.
Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz. 2020 · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, et al. 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Mosaicking to distill: Knowledge distillation from out-of-domain data
Gongfan Fang, Yifan Bao, Jie Song, Xinchao Wang, Donglin Xie, Chengchao Shen, and Mingli Song. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021 · 2021
Earlier work this paper cites.
Distilling linguistic context for language model compression
Geondo Park, Gyeongman Kim, and Eunho Yang. 2021 · 2021
Earlier work this paper cites.
MiniLMv2: Multi-head self-attention relation distillation for compressing pretrained transformers
Wenhui Wang, Hangbo Bao, Shaohan Huang, Li Dong, and Furu Wei. 2021 · 2021
Earlier work this paper cites.
Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
Earlier work this paper cites.
Multi-granularity structural knowledge distillation for language model compression
Chang Liu, Chongyang Tao, Jiazhan Feng, and Dongyan Zhao. 2022 · 2022
Cited alongside, same era.
Introducing chatgpt
OpenAI. 2022 · 2022
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Scharli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed Huai hsin Chi, Denny Zhou, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Generalized knowledge distillation for auto-regressive language models
Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, and Olivier Bachem. 2023 · 2023
Cited alongside, same era.
Explicit knowledge transfer for weakly-supervised code generation
Zhangir Azerbayev, Ansong Ni, Hailey Schoelkopf, and Dragomir Radev. 2023 · 2023
Cited alongside, same era.
Orca: Progressive learning from complex explanation traces of gpt-4
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Hassan Awadallah. 2023 · 2023
Later among the works it cites.
Gpt-4 is openai’s most advanced system, producing safer and more useful responses
OpenAI. 2023 · 2023
Later among the works it cites.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Later among the works it cites.
f-divergence minimization for sequence-level knowledge distillation
Yuqiao Wen, Zichao Li, Wenyu Du, and Lili Mou. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
Cited alongside, same era.
MCC-KD: Multi-CoT consistent knowledge distillation
Hongzhan Chen, Siyue Wu, Xiaojun Quan, Rui Wang, Ming Yan, and Ji Zhang. 2023 · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
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 · 2023
Cited alongside, same era.
Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023 · 2023
Cited alongside, same era.
Tora: A tool-integrated reasoning agent for mathematical problem solving
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. 2023 · 2023
Cited alongside, same era.
Minillm: Knowledge distillation of large language models
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2023 · 2023
Cited alongside, same era.
Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
AD-KD: Attribution-driven knowledge distillation for language model compression
Siyue Wu, Hongzhan Chen, Xiaojun Quan, Qifan Wang, and Rui Wang. 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
Measuring massive multitask chinese understanding
Hui Zeng. 2023 · 2023
Later among the works it cites.
Agieval: A human-centric benchmark for evaluating foundation models
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied Sanosi Saied, Weizhu Chen, and Nan Duan. 2023 · 2023
Later among the works it cites.
Starling-7b: Improving llm helpfulness and harmlessness with rlaif
Banghua Zhu, Evan Frick, Tianhao Wu, Hanlin Zhu, and Jiantao Jiao. 2023 · 2023
Later among the works it cites.
On-policy distillation of language models: Learning from self-generated mistakes
Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem. 2024 · 2024
Closest in time.
Claude 3 family
Anthropic. 2024 · 2024
Closest in time.
Common 7b language models already possess strong math capabilities
Chen Li, Weiqi Wang, Jingcheng Hu, Yixuan Wei, Nanning Zheng, Han Hu, Zheng Zhang, and Houwen Peng. 2024 · 2024
Closest in time.
Introducing meta llama 3: The most capable openly available llm to date
MetaAI. 2024 · 2024
Closest in time.
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
Closest in time.