6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Towards Optimal Learning of Language Models
Yuxian Gu, Li Dong, Yaru Hao +3
This work studies the general principles of improving the learning of language models (LMs), which aims at reducing the necessary training steps for achieving superior performance.…
cs.CL2024★ 6 cited
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang +17
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on se…
cs.CL2023
Pre-Training to Learn in Context
Yuxian Gu, Li Dong, Furu Wei +1
In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP communi…