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The modern autoregressive Large Language Models (LLMs) have achieved outstanding performance on NLP benchmarks, and they are deployed in the real world.
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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Continuous-Time Markov Chains
William J. Anderson. 1991 · 1991
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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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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen. 2005 · 2005
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2006
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Tweedie’s formula and selection bias
Bradley Efron. 2011 · 2011
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Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021 · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent. 2011 · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge. 2016 · 2016
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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: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Openwebtext corpus
Aaron Gokaslan and Vanya Cohen. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. 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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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. 2021 · 2021
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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, et al. 2021 · 2021
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A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. 2021 · 2021
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré. 2021 · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling. 2021 · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
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Mamba: Linear-time sequence modeling with selective state spaces
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 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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Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon. 2023 · 2023
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Concrete score matching: Generalized score matching for discrete data
Chenlin Meng, Kristy Choi, Jiaming Song, and Stefano Ermon. 2023 · 2023
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Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, and Arnaud Doucet. 2022 · 2022
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Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale
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Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, and Jonas Adler. 2022 · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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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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Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. 2022 · 2022
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Resurrecting recurrent neural networks for long sequences
Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De. 2023 · 2023
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Scalable diffusion models with transformers
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Hyena hierarchy: Towards larger convolutional language models
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Score-based continuous-time discrete diffusion models
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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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
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