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Model merging is to combine fine-tuned models derived from multiple domains, with the intent of enhancing the model's proficiency across various domains.
Www’18 open challenge: Financial opinion mining and question answering
Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018 · 1942
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael Mozer and Paul Smolensky. 1988 · 1988
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Multiparty differential privacy via aggregation of locally trained classifiers
Manas A. Pathak, Shantanu Rane, and Bhiksha Raj. 2010 · 2010
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Good debt or bad debt: Detecting semantic orientations in economic texts
Pekka Malo, Ankur Sinha, Pekka J. Korhonen, Jyrki Wallenius, and Pyry Takala. 2014 · 2014
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William J. Dally. 2015a · 2015
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Convergent learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John E. Hopcroft. 2016 · 2016
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Topology and geometry of half-rectified network optimization
C. Daniel Freeman and Joan Bruna. 2017 · 2017
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. 2018 · 2018
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Towards optimal structured CNN pruning via generative adversarial learning
Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, and David S. Doermann. 2019 · 2019
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CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service
Marco Lippi, Przemyslaw Palka, Giuseppe Contissa, Francesca Lagioia, Hans-Wolfgang Micklitz, Giovanni Sartor, and Paolo Torroni. 2019 · 2019
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Supreme court database, version 2020 release 01
Harold J. Spaeth, Lee Epstein, Jeffrey A. Segal, Andrew D. Martin, Theodore J. Ruger, and Sara C. Benesh. 2020 · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, and Yasaman Khazaeni. 2020 · 2020
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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
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Measuring mathematical problem solving with the MATH dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Accelerated sparse neural training: A provable and efficient method to find N: M transposable masks
Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, and Daniel Soudry. 2021 · 2021
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A survey on federated learning
Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. 2021 · 2021
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MEDITRON-70B: scaling medical pretraining for large language models
Zeming Chen, Alejandro Hernández-Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, and Antoine Bosselut. 2023 · 2023
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Publicly shareable clinical large language model built on synthetic clinical notes
Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, Jungwoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, and Edward Choi. 2023 · 2023
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Modelkeeper: Accelerating DNN training via automated training warmup
Fan Lai, Yinwei Dai, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2023 · 2023
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Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023 · 2023
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Task arithmetic in the tangent space: Improved editing of pre-trained models
Guillermo Ortiz-Jiménez, Alessandro Favero, and Pascal Frossard. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Daixuan Cheng, Shaohan Huang, and Furu Wei. 2023 · 2023
Cited alongside, same era.
Generative ai for math: Abel
Ethan Chern, Haoyang Zou, Xuefeng Li, Jiewen Hu, Kehua Feng, Junlong Li, and Pengfei Liu. 2023 · 2023
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Task arithmetic with lora for continual learning
Rajas Chitale, Ankit Vaidya, Aditya Kane, and Archana Ghotkar. 2023 · 2023
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Language and task arithmetic with parameter-efficient layers for zero-shot summarization
Alexandra Chronopoulou, Jonas Pfeiffer, Joshua Maynez, Xinyi Wang, Sebastian Ruder, and Priyanka Agrawal. 2023 · 2023
Cited alongside, same era.
Sparsegpt: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh. 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.
Editing models with task arithmetic
Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023 · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
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
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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 · 2023
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Augustin Toma, Patrick R. Lawler, Jimmy Ba, Rahul G. Krishnan, Barry B. Rubin, and Bo Wang. 2023 · 2023
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PIXIU: A large language model, instruction data and evaluation benchmark for finance
Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. 2023 · 2023
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Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. 2023 · 2023
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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
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Scaling relationship on learning mathematical reasoning with large language models
Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Chuanqi Tan, and Chang Zhou. 2023 · 2023
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Mammoth: Building math generalist models through hybrid instruction tuning
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. 2023 · 2023
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A survey on model compression for large language models
Xunyu Zhu, Jian Li, Yong Liu, Can Ma, and Weiping Wang. 2023 · 2023
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