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The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020b · 1904
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Rethinking exposure bias in adversarial language modeling
Yifan Xu, Kening Zhang, Haoyu Dong, Yuezhou Sun, Wenlong Zhao, and Tu Zhuowen. 2020 · 1910
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Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
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Stacked generalization
David H Wolpert. 1992 · 1992
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Bagging predictors
Leo Breiman. 1996 · 1996
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire. 1997 · 1997
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Ensemble methods in machine learning
Thomas G Dietterich. 2000 · 2000
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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Scaling large learning problems with hard parallel mixtures
Ronan Collobert, Yoshua Bengio, and Samy Bengio. 2003 · 2003
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Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke. 2005 · 2005
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Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever. 2013 · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015 · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
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Trainable greedy decoding for neural machine translation
Jiatao Gu, Kyunghyun Cho, and Victor O. K. Li. 2017 · 2017
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Routing networks: Adaptive selection of non-linear functions for multi-task learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, and Andrew Gordon Wilson. 2018 · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
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Ensemble learning: A survey
Omer Sagi and Lior Rokach. 2018 · 2018
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Blockwise parallel decoding for deep autoregressive models
Mitchell Stern, Noam Shazeer, and Jakob Uszkoreit. 2018 · 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. 2019 · 2019
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Mohammad Saleh, and Peter J. Liu. 2019 · 2019
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020 · 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 · 2020
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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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GeDi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2021 · 2021
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Dexperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A Smith, and Yejin Choi. 2021 · 2021
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Looking beyond sentence-level natural language inference for question answering and text summarization
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, and Kartik Talamadupula. 2021 · 2021
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Entity-level factual consistency of abstractive text summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cícero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, and Bing Xiang. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 2021
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FUDGE: Controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
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QAFactEval: Improved QA-based factual consistency evaluation for summarization
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022 · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022
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Attributed text generation via post-hoc research and revision
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, N. Lao, Hongrae Lee, Da-Cheng Juan, and Kelvin Guu. 2022 · 2022
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Tao Guo, Song Guo, Junxiao Wang, and Wenchao Xu. 2022 · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022 · 2022
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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. 2022 · 2022
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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2022
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Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane A. Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 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 · 2022
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Branch-train-merge: Embarrassingly parallel training of expert language models
Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A Smith, and Luke Zettlemoyer. 2022 · 2022
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Few-shot learning with multilingual generative language models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, and Xian Li. 2022 · 2022
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Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel. 2022 · 2022
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Varifocal question generation for fact-checking
Nedjma Djouhra Ousidhoum, Moy Yuan, and Andreas Vlachos. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Diverse weight averaging for out-of-distribution generalization
Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, and Matthieu Cord. 2022 · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022 · 2022
Cited alongside, same era.
Rationale-augmented ensembles in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 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.
End-to-end multimodal fact-checking and explanation generation: A challenging dataset and models
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.
Speculative contrastive decoding
Hongyi Yuan, Keming Lu, Fei Huang, Zheng Yuan, and Chang Zhou. 2023 · 2023
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GLM-130b: An open bilingual pre-trained model
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang. 2023 · 2023
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Tablegpt: Towards unifying tables, nature language and commands into one gpt
Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, Tao Zhang, Chen Zhou, Kaizhe Shou, Miao Wang, Wufang Zhu, Guoshan Lu, Chao Ye, Yali Ye, Wentao Ye, Yiming Zhang, Xinglong Deng, Jie Xu, Haobo Wang, Gang Chen, and Junbo Zhao. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Barry Menglong Yao, Aditya Shah, Lichao Sun, Jin-Hee Cho, and Lifu Huang. 2022 · 2022
Cited alongside, same era.
Examining scaling and transfer of language model architectures for machine translation
Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier Garcia, Jonathan Shen, and Orhan Firat. 2022 · 2022
Cited alongside, same era.
Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. 2023 · 2023
Cited alongside, same era.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023 · 2023
Cited alongside, same era.
Llemma: An open language model for mathematics
Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q Jiang, Jia Deng, Stella Biderman, and Sean Welleck. 2023 · 2023
Cited alongside, same era.
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.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023 · 2023
Cited alongside, same era.
Model merging by uncertainty-based gradient matching
Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, and Mohammad Emtiyaz Khan. 2023 · 2023
Cited alongside, same era.
Yuanzhao Zhai, Han Zhang, Yu Lei, Yue Yu, Kele Xu, Dawei Feng, Bo Ding, and Huaimin Wang. 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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Scalable ensembling for mitigating reward overoptimisation
Ahmed M Ahmed, Rafael Rafailov, Stepan Sharkov, Xuechen Li, and Sanmi Koyejo. 2024 · 2024
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Evolutionary optimization of model merging recipes
Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, and David Ha. 2024 · 2024
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Prompt-saw: Leveraging relation-aware graphs for textual prompt compression
Muhammad Asif Ali, Zhengping Li, Shu Yang, Keyuan Cheng, Yang Cao, Tianhao Huang, Lijie Hu, Lu Yu, and Di Wang. 2024 · 2024
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Hallucination of multimodal large language models: A survey
Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, and Mike Zheng Shou. 2024 · 2024
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Combining insights from multiple large language models improves diagnostic accuracy
Gioele Barabucci, Victor Shia, Eugene Chu, Benjamin Harack, and Nathan Fu. 2024 · 2024
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024 · 2024
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Inside: Llms’ internal states retain the power of hallucination detection
Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye. 2024 · 2024
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Reward model ensembles help mitigate overoptimization
Thomas Coste, Usman Anwar, Robert Kirk, and David Krueger. 2024 · 2024
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Della-merging: Reducing interference in model merging through magnitude-based sampling
Pala Tej Deep, Rishabh Bhardwaj, and Soujanya Poria. 2024 · 2024
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Hybrid llm: Cost-efficient and quality-aware query routing
Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Ruhle, Laks VS Lakshmanan, and Ahmed Hassan Awadallah. 2024 · 2024
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Fedmkt: Federated mutual knowledge transfer for large and small language models
Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Lixin Fan, and Qiang Yang. 2024 · 2024
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Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024 · 2024
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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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Satya Kesav Gundabathula and Sriram R Kolar. 2024 · 2024
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Large language model based multi-agents: A survey of progress and challenges
Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. 2024 · 2024
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Model merging and safety alignment: One bad model spoils the bunch
Hasan Abed Al Kader Hammoud, Umberto Michieli, Fabio Pizzati, Philip Torr, Adel Bibi, Bernard Ghanem, and Mete Ozay. 2024 · 2024
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G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. 2024 · 2024
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Routerbench: A benchmark for multi-llm routing system
Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. 2024 · 2024
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Model stock: All we need is just a few fine-tuned models
Dong-Hwan Jang, Sangdoo Yun, and Dongyoon Han. 2024 · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. 2024 · 2024
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Prometheus 2: An open source language model specialized in evaluating other language models
Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, and Minjoon Seo. 2024 · 2024
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Yongqiang Ma, Lizhi Qin, Jiawei Liu, Yangyang Kang, Yue Zhang, Wei Lu, Xiaozhong Liu, and Qikai Cheng. 2024 · 2024
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Rafe: Ranking feedback improves query rewriting for rag
Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. 2024 · 2024
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Pack of llms: Model fusion at test-time via perplexity optimization
Costas Mavromatis, Petros Karypis, and George Karypis. 2024 · 2024
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Leeroo orchestrator: Elevating llms performance through model integration
Alireza Mohammadshahi, Ali Shaikh, and Majid Yazdani. 2024 · 2024
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Fisher mask nodes for language model merging
Ganesh Nathan et al. 2024 · 2024
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Parameter-efficient detoxification with contrastive decoding
Tong Niu, Caiming Xiong, Semih Yavuz, and Yingbo Zhou. 2024 · 2024
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Lossless acceleration of large language model via adaptive n-gram parallel decoding
Jie Ou, Yueming Chen, and Wenhong Tian. 2024 · 2024
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Llmlingua-2: Data distillation for efficient and faithful task-agnostic prompt compression
Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, and Dongmei Zhang. 2024 · 2024
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Phuc Phan, Hieu Tran, and Long Phan. 2024 · 2024
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Fanyi Qu, Hao Sun, and Yunfang Wu. 2024 · 2024
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Warm: On the benefits of weight averaged reward models
Alexandre Ram’e, Nino Vieillard, L’eonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, and Johan Ferret. 2024 · 2024
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Warm: On the benefits of weight averaged reward models
Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, and Johan Ferret. 2024 · 2024
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Unchosen experts can contribute too: Unleashing moe models’ power by self-contrast
Chufan Shi, Cheng Yang, Xinyu Zhu, Jiahao Wang, Taiqiang Wu, Siheng Li, Deng Cai, Yujiu Yang, and Yu Meng. 2024 · 2024
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Harnessing the power of multiple minds: Lessons learned from llm routing
KV Srivatsa, Kaushal Kumar Maurya, and Ekaterina Kochmar. 2024 · 2024
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Zipit! merging models from different tasks without training
George Stoica, Daniel Bolya, Jakob Bjorner, Pratik Ramesh, Taylor Hearn, and Judy Hoffman. 2024 · 2024
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Xin Su, Tiep Le, Steven Bethard, and Phillip Howard. 2024 · 2024
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Llm-based multi-agent reinforcement learning: Current and future directions
Chuanneng Sun, Songjun Huang, and Dario Pompili. 2024 · 2024
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Minicheck: Efficient fact-checking of llms on grounding documents
Liyan Tang, Philippe Laban, and Greg Durrett. 2024 · 2024
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Yao-Hung Tsai, Walter Talbott, and Jian Zhang. 2024 · 2024
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Contrastive decoding reduces hallucinations in large multilingual machine translation models
Jonas Waldendorf, Barry Haddow, and Alexandra Birch. 2024 · 2024
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Pmc-llama: toward building open-source language models for medicine
Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Weidi Xie, and Yanfeng Wang. 2024 · 2024
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Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, and Zhifang Sui. 2024 · 2024
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Openfedllm: Training large language models on decentralized private data via federated learning
Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li, Zexi Li, Yinda Xu, Yaxin Du, Yanfeng Wang, and Siheng Chen. 2024 · 2024
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Yao-Ching Yu, Chun-Chih Kuo, Ziqi Ye, Yu-Cheng Chang, and Yueh-Se Li. 2024 · 2024
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Large language model cascades with mixture of thought representations for cost-efficient reasoning
Murong Yue, Jie Zhao, Min Zhang, Liang Du, and Ziyu Yao. 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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