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Speculative decoding (SD) has attracted a significant amount of research attention due to the substantial speedup it can achieve for LLM inference.
Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin. 2019 · 1909
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 1910
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Accelerating feedforward computation via parallel nonlinear equation solving
Yang Song, Chenlin Meng, Renjie Liao, and Stefano Ermon. 2021 · 2002
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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On the effect of dropping layers of pre-trained transformer models
Hassan Sajjad, Fahim Dalvi, Nadir Durrani, and Preslav Nakov. 2022 · 2004
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Structured pruning of deep convolutional neural networks
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung. 2017 · 2017
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BranchyNet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and H. T. Kung. 2017 · 2017
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Learning sparse neural networks through l 0 l_{0} regularization
Christos Louizos, Max Welling, and Diederik P. Kingma. 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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Blockwise parallel decoding for deep autoregressive models
Mitchell Stern, Noam Shazeer, and Jakob Uszkoreit. 2018 · 2018
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
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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. 2021 · 2021
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Block pruning for faster transformers
François Lagunas, Ella Charlaix, Victor Sanh, and Alexander M. Rush. 2021 · 2021
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Instantaneous grammatical error correction with shallow aggressive decoding
Xin Sun, Tao Ge, Furu Wei, and Houfeng Wang. 2021 · 2021
Cited alongside, same era.
LLM.int8(): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Structured pruning learns compact and accurate models
Mengzhou Xia, Zexuan Zhong, and Danqi Chen. 2022 · 2022
Cited alongside, same era.
Gemini: A family of highly capable multimodal models
Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Fast and robust early-exiting framework for autoregressive language models with synchronized parallel decoding
Sangmin Bae, Jongwoo Ko, Hwanjun Song, and Se-Young Yun. 2023 · 2023
Medusa: Simple LLM inference acceleration framework with multiple decoding heads
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, and Tri Dao. 2024 · 2024
Closest in time.
Sequoia: Scalable, robust, and hardware-aware speculative decoding
Zhuoming Chen, Avner May, Ruslan Svirschevski, Yuhsun Huang, Max Ryabinin, Zhihao Jia, and Beidi Chen. 2024 · 2024
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LayerSkip: Enabling early exit inference and self-speculative decoding
Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed A Aly, Beidi Chen, and Carole-Jean Wu. 2024 · 2024
Closest in time.
Break the sequential dependency of LLM inference using lookahead decoding
Yichao Fu, Peter Bailis, Ion Stoica, and Hao Zhang. 2024 · 2024
Closest in time.
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Cited alongside, same era.
Accelerating large language model decoding with speculative sampling
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. 2023 · 2023
Cited alongside, same era.
Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao. 2023 · 2023
Cited alongside, same era.
Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
Cited alongside, same era.
LLM-Pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023 · 2023
Cited alongside, same era.
Accelerating transformer inference for translation via parallel decoding
Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, and Emanuele Rodola. 2023 · 2023
Cited alongside, same era.
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. 2023 · 2023
Cited alongside, same era.
Accelerating LLM inference with staged speculative decoding
Benjamin Spector and Chris Re. 2023 · 2023
Cited alongside, same era.
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, and Gabriel Synnaeve. 2024 · 2024
Closest in time.
CLLMs: Consistency large language models
Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, and Hao Zhang. 2024 · 2024
Closest in time.
Eagle: Speculative sampling requires rethinking feature uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. 2024 · 2024
Closest in time.
QServe: W4A8KV4 quantization and system co-design for efficient LLM serving
Yujun Lin, Haotian Tang, Shang Yang, Zhekai Zhang, Guangxuan Xiao, Chuang Gan, and Song Han. 2024 · 2024
Closest in time.
SpecInfer: Accelerating large language model serving with tree-based speculative inference and verification
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Zeyu Wang, Zhengxin Zhang, Rae Ying Yee Wong, Alan Zhu, Lijie Yang, Xiaoxiang Shi, Chunan Shi, Zhuoming Chen, Daiyaan Arfeen, Reyna Abhyankar, and Zhihao Jia. 2024 · 2024
Closest in time.
Tycho Ouderaa, Markus Nagel, Mart van Baalen, Yuki M. Asano, and Tijmen Blankevoort. 2024 · 2024
Closest in time.
Mixture-of-depths: Dynamically allocating compute in transformer-based language models
David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, and Adam Santoro. 2024 · 2024
Closest in time.
Layer-Condensed KV Cache for efficient inference of large language models
Haoyi Wu and Kewei Tu. 2024 · 2024
Closest in time.
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, and Zhifang Sui. 2024 · 2024
Closest in time.
Multi-candidate speculative decoding
Sen Yang, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2024 · 2024
Closest in time.
Beyond the speculative game: A survey of speculative execution in large language models
Chen Zhang, Zhuorui Liu, and Dawei Song. 2024 · 2024
Closest in time.
Ouroboros: Speculative decoding with large model enhanced drafting
Weilin Zhao, Yuxiang Huang, Xu Han, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2024 · 2024
Closest in time.
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2048
Closest in time.