To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis

To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis

22 May 2023

Wangchunshu Zhou

Yang You

Papers citing "To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis"

6 / 6 papers shown

Title
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 276 8,441 0 04 Mar 2022
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers Yi Tay Mostafa Dehghani J. Rao W. Fedus Samira Abnar Hyung Won Chung Sharan Narang Dani Yogatama Ashish Vaswani Donald Metzler 162 89 0 22 Sep 2021
Scalable and Efficient MoE Training for Multitask Multilingual Models Young Jin Kim A. A. Awan Alexandre Muzio Andres Felipe Cruz Salinas Liyang Lu Amr Hendy Samyam Rajbhandari Yuxiong He Hany Awadalla MoE 73 69 0 22 Sep 2021
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 215 3,054 0 23 Jan 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 226 1,436 0 17 Sep 2019
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 265 9,997 0 01 Sep 2014