4.3k citations · 10.6k across the 26 of their papers we have counts for
Showing 2023 · cs.CLShow all
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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★ 7 cited
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Shayne Longpre, Gregory Yauney, Emily Reif +8
Pretraining is the preliminary and fundamental step in developing capable language models (LM). Despite this, pretraining data design is critically under-documented and often guide…
cs.CL2023★ 100 cited
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay +8
We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with…