Fetching the paper…
Reading the bibliography…
Autoregressive sampling from large language models has led to state-of-the-art results in several natural language tasks.
On the translocation of masses
Leonid V Kantorovich · 1942
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
Earlier work this paper cites.
Linear programming
George B Dantzig · 2002
Earlier work this paper cites.
Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
Earlier work this paper cites.
Optimal transport: old and new
Cédric Villani et al · 2009
Earlier work this paper cites.
Probability theory: The coupling method
Frank Den Hollander · 2012
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2013
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Earlier work this paper cites.
Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow
Yin Tat Lee and Aaron Sidford · 2014
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg · 2017
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin · 2018
Cited alongside, same era.
Blockwise parallel decoding for deep autoregressive models
Mitchell Stern, Noam Shazeer, and Jakob Uszkoreit · 2018
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same 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
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
Closest in time.
Introducing PaLM 2, 2023
Google AI · 2023
Closest in time.
PaLM 2 technical report, 2023
Google PaLM-2 Team · 2023
Closest in time.
Flax: A neural network library and ecosystem for JAX, 2023
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2023
Closest in time.
Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias · 2023
Closest in time.
Eagle: Lossless acceleration of llm decoding by feature extrapolation, 2023
Yuhui Li, Chao Zhang, and Hongyang Zhang · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fast algorithms for computational optimal transport and wasserstein barycenter
Wenshuo Guo, Nhat Ho, and Michael Jordan · 2020
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 · 2022
Cited alongside, same era.
Lossless acceleration for seq2seq generation with aggressive decoding
Tao Ge, Heming Xia, Xin Sun, Si-Qing Chen, and Furu Wei · 2022
Cited alongside, same era.
Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 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
Cited alongside, same era.
Closest in time.
Specinfer: Accelerating generative large language model serving with speculative inference and token tree verification, 2023
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Zeyu Wang, Rae Ying Yee Wong, Alan Zhu, Lijie Yang, Xiaoxiang Shi, Chunan Shi, Zhuoming Chen, Daiyaan Arfeen, Reyna Abhyankar, and Zhihao Jia · 2023
Closest in time.
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
Closest in time.
Inference with reference: Lossless acceleration of large language models
Nan Yang, Tao Ge, Liang Wang, Binxing Jiao, Daxin Jiang, Linjun Yang, Rangan Majumder, and Furu Wei · 2023
Closest in time.