Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

8 April 2024

Papers citing "Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models"

7 / 7 papers shown

Title
Mixture of Parrots: Experts improve memorization more than reasoning Samy Jelassi Clara Mohri David Brandfonbrener Alex Gu Nikhil Vyas Nikhil Anand David Alvarez-Melis Yuanzhi Li Sham Kakade Eran Malach MoE 28 4 0 24 Oct 2024
Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition Artem Basharin Andrei Chertkov Ivan V. Oseledets 34 1 0 23 Oct 2024
Mixture of Attention Heads: Selecting Attention Heads Per Token Xiaofeng Zhang Yikang Shen Zeyu Huang Jie Zhou Wenge Rong Zhang Xiong MoE 93 42 0 11 Oct 2022
Mixture-of-Experts with Expert Choice Routing Yan-Quan Zhou Tao Lei Han-Chu Liu Nan Du Yanping Huang Vincent Zhao Andrew M. Dai Zhifeng Chen Quoc V. Le James Laudon MoE 147 323 0 18 Feb 2022
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference Sneha Kudugunta Yanping Huang Ankur Bapna M. Krikun Dmitry Lepikhin Minh-Thang Luong Orhan Firat MoE 119 104 0 24 Sep 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 245 1,977 0 31 Dec 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 223 4,424 0 23 Jan 2020