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Large language models (LLMs) exhibit exceptional performance across various downstream tasks.
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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Learning deep transformer models for machine translation
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Once-for-all: Train one network and specialize it for efficient deployment
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Rouge: A package for automatic evaluation of summaries
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The right tool for the job: Matching model and instance complexities
Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, and Noah A Smith. 2020 · 2004
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Deebert: Dynamic early exiting for accelerating bert inference
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin. 2020 · 2004
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Comet: A neural framework for mt evaluation
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2020 · 2012
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Conditional deep learning for energy-efficient and enhanced pattern recognition
Priyadarshini Panda, Abhronil Sengupta, and Kaushik Roy. 2016 · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. 2016 · 2016
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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger. 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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Bert loses patience: Fast and robust inference with early exit
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. 2020 · 2020
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Faster depth-adaptive transformers
Yijin Liu, Fandong Meng, Jie Zhou, Yufeng Chen, and Jinan Xu. 2021 · 2021
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Consistent accelerated inference via confident adaptive transformers
Tal Schuster, Adam Fisch, Tommi Jaakkola, and Regina Barzilay. 2021 · 2021
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Early exiting with ensemble internal classifiers
Tianxiang Sun, Yunhua Zhou, Xiangyang Liu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, and Xipeng Qiu. 2021 · 2021
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Parsa Kavehzadeh, Mojtaba Valipour, Marzieh Tahaei, Ali Ghodsi, Boxing Chen, and Mehdi Rezagholizadeh. 2023 · 2023
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Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
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A global past-future early exit method for accelerating inference of pre-trained language models
Kaiyuan Liao, Yi Zhang, Xuancheng Ren, Qi Su, Xu Sun, and Bin He. 2021 · 2023
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OpenAI. 2023 · 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 · 2023
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Berxit: Early exiting for bert with better fine-tuning and extension to regression
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Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Ro Wang, and Yoav Goldberg. 2022 · 2022
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Confident adaptive language modeling
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Tran, Yi Tay, and Donald Metzler. 2022 · 2022
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Skipdecode: Autoregressive skip decoding with batching and caching for efficient llm inference
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A paradigm shift in machine translation: Boosting translation performance of large language models
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Layer skip: Enabling early exit inference and self-speculative decoding
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Sorted llama: Unlocking the potential of intermediate layers of large language models for dynamic inference
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Predictor-corrector enhanced transformers with exponential moving average coefficient learning
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