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Model merging offers an effective strategy to combine the strengths of multiple finetuned models into a unified model that preserves the specialized capabilities of each.
Sparse matrix technology-electronic edition
Sergio Pissanetzky · 1984
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
The trec-8 question answering track report
Ellen M Voorhees et al · 1999
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee · 2004
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
Bill Dolan and Chris Brockett · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
Earlier work this paper cites.
Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie · 2005
Earlier work this paper cites.
The second pascal recognising textual entailment challenge
R Bar Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor · 2006
Earlier work this paper cites.
The third pascal recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and William B Dolan · 2007
Earlier work this paper cites.
The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Mnist handwritten digit database, 2010
Yann LeCun, Corinna Cortes, Chris Burges, et al · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al · 2011
Earlier work this paper cites.
The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
Earlier work this paper cites.
Matrix computations
Gene H Golub and Charles F Van Loan · 2013
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
Earlier work this paper cites.
Jimmy Lei Ba · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Sun database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
Earlier work this paper cites.
Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
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
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
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Git re-basin: Merging models modulo permutation symmetries
Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2022
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Fusing finetuned models for better pretraining
Leshem Choshen, Elad Venezian, Noam Slonim, and Yoav Katz · 2022
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Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng · 2022
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Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel · 2022
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Direct and indirect effects
Judea Pearl · 2022
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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On iterative neural network pruning, reinitialization, and the similarity of masks
Michela Paganini and Jessica Forde · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander Rush · 2020
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Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi · 2020
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Platon: Pruning large transformer models with upper confidence bound of weight importance
Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, and Tuo Zhao · 2022
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Knowledge is a region in weight space for fine-tuned language models
Almog Gueta, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, and Leshem Choshen · 2023
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2023
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Effective and parameter-efficient reusing fine-tuned models
Weisen Jiang, Baijiong Lin, Han Shi, Yu Zhang, James T Kwok, et al · 2023
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Investigating forgetting in pre-trained representations through continual learning
Yun Luo, Zhen Yang, Xuefeng Bai, Fandong Meng, Jie Zhou, and Yue Zhang · 2023
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Task-specific skill localization in fine-tuned language models
Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, and Sanjeev Arora · 2023
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A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter · 2023
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Merging by matching models in task subspaces
Derek Tam, Mohit Bansal, and Colin Raffel · 2023
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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
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Sheared llama: Accelerating language model pre-training via structured pruning
Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, and Danqi Chen · 2023
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TIES-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
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Adamerging: Adaptive model merging for multi-task learning
Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, and Dacheng Tao · 2023
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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
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Loraprune: Pruning meets low-rank parameter-efficient fine-tuning
Mingyang Zhang, Hao Chen, Chunhua Shen, Zhen Yang, Linlin Ou, Xinyi Yu, and Bohan Zhuang · 2023
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A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations
Hongrong Cheng, Miao Zhang, and Javen Qinfeng Shi · 2024
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Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun · 2024
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Robust multi-task learning with excess risks
Yifei He, Shiji Zhou, Guojun Zhang, Hyokun Yun, Yi Xu, Belinda Zeng, Trishul Chilimbi, and Han Zhao · 2024
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Revisiting scalarization in multi-task learning: A theoretical perspective
Yuzheng Hu, Ruicheng Xian, Qilong Wu, Qiuling Fan, Lang Yin, and Han Zhao · 2024
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Population parameter averaging (PAPA)
Alexia Jolicoeur-Martineau, Emy Gervais, Kilian FATRAS, Yan Zhang, and Simon Lacoste-Julien · 2024
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Realistic evaluation of model merging for compositional generalization
Derek Tam, Yash Kant, Brian Lester, Igor Gilitschenski, and Colin Raffel · 2024
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Merging multi-task models via weight-ensembling mixture of experts
Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, and Dacheng Tao · 2024
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Apt: Adaptive pruning and tuning pretrained language models for efficient training and inference
Bowen Zhao, Hannaneh Hajishirzi, and Qingqing Cao · 2024
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