7 citations · 11 across the 7 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.CL2024
Learning from "Silly" Questions Improves Large Language Models, But Only Slightly
Tingyuan Zhu, Shudong Liu, Yidong Wang +4
Constructing high-quality Supervised Fine-Tuning (SFT) datasets is critical for the training of large language models (LLMs). Recent studies have shown that using data from a speci…
cs.CL2024★ 7 cited
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
cs.LG2024★ 2 cited
A Note on LoRA
Vlad Fomenko, Han Yu, Jongho Lee +2
LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the or…