1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2024
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models
Xiaolin Hu, Xiang Cheng, Peiyu Liu +4
Low-rank adaptation (LoRA) reduces the computational and memory demands of fine-tuning large language models (LLMs) by approximating updates with low-rank matrices. However, low-ra…
cs.CL2023★ 1 cited
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Peiyu Liu, Zikang Liu, Ze-Feng Gao +5
Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…
cs.CL2023
Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
Peiyu Liu, Ze-Feng Gao, Yushuo Chen +2
In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters…