2.1k citations · 4.2k across the 21 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.CL2023★ 20 cited
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sheng Shen, Le Hou, Yanqi Zhou +17
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.…
cs.CL2023★ 6 cited
UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining
Hyung Won Chung, Noah Constant, Xavier Garcia +4
Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systema…
cs.AI2023★ 113 cited
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Shayne Longpre, Le Hou, Tu Vu +8
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022). Through careful ablation studies o…