2 citations · 4 across the 5 of their papers we have counts for
5 papers
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning
Yiming Shi, Jiwei Wei, Yujia Wu +4
The rapid growth of model scale has necessitated substantial computational resources for fine-tuning. Existing approach such as Low-Rank Adaptation (LoRA) has sought to address the…
Machine Translation Evaluation Benchmark for Wu Chinese: Workflow and Analysis
Hongjian Yu, Yiming Shi, Zherui Zhou +1
We introduce a FLORES+ dataset as an evaluation benchmark for modern Wu Chinese machine translation models and showcase its compatibility with existing Wu data. Wu Chinese is mutua…
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
Chengwei Sun, Jiwei Wei, Yujia Wu +5
Large pre-trained models (LPMs) have demonstrated exceptional performance in diverse natural language processing and computer vision tasks. However, fully fine-tuning these models…
Rhyme-aware Chinese lyric generator based on GPT
Yixiao Yuan, Yangchen Huang, Yu Ma +4
Neural language representation models such as GPT, pre-trained on large-scale corpora, can effectively capture rich semantic patterns from plain text and be fine-tuned to consisten…
DiffLoRA: Generating Personalized Low-Rank Adaptation Weights with Diffusion
Yujia Wu, Yiming Shi, Jiwei Wei +3
Personalized text-to-image generation has gained significant attention for its capability to generate high-fidelity portraits of specific identities conditioned on user-defined pro…