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With the proliferation of domain-specific models, model merging has emerged as a set of techniques that combine the capabilities of multiple models into one that can multitask without the cost of additional training.
On the mathematical foundations of theoretical statistics
Ronald A Fisher. 1922 · 1922
Earlier work this paper cites.
Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
Earlier work this paper cites.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2017
Earlier work this paper cites.
Optimization based layer-wise magnitude-based pruning for dnn compression
Guiying Li, Chao Qian, Chunhui Jiang, Xiaofen Lu, and Ke Tang. 2018 · 2018
Earlier work this paper cites.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2019 · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 2019
Earlier work this paper cites.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, 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, Christopher Hesse, and John Schulman. 2021 · 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 · 2021
Earlier work this paper cites.
Layer-adaptive sparsity for the magnitude-based pruning
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel. 2022 · 2022
Cited alongside, same era.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. 2022 · 2022
Cited alongside, same era.
Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023 · 2023
Later among the works it cites.
Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity
Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, and Shiwei Liu. 2023 · 2023
Later among the works it cites.
Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. 2023 · 2023
Later among the works it cites.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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SparseGPT: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh. 2023 · 2023
Cited alongside, same era.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023 · 2023
Cited alongside, same era.
Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2023 · 2023
Cited alongside, same era.
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
A survey on model compression for large language models
Xunyu Zhu, Jian Li, Yong Liu, Can Ma, and Weiping Wang. 2023 · 2023
Later among the works it cites.
Arcee’s mergekit: A toolkit for merging large language models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vlad Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024 · 2024
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Leveraging large language models for nlg evaluation: A survey
Zhen Li, Xiaohan Xu, Tao Shen, Can Xu, Jia-Chen Gu, and Chongyang Tao. 2024 · 2024
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Wizardcoder: Empowering code large language models with evol-instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2024 · 2024
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A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. 2024 · 2024
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WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Qingwei Lin, and Daxin Jiang. 2024 · 2024
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Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal. 2024 · 2024
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Dppa: Pruning method for large language model to model merging
Yaochen Zhu, Rui Xia, and Jiajun Zhang. 2024 · 2024
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