5 papers
NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion
Chuheng Chen, Xiaofei Zhou, Geyuan Zhang +1
Low-Rank Adaptation (LoRA) fusion enables the composition of subject and style representations for controllable generation without retraining. However, existing approaches primaril…
Learning Depth from Past Selves: Self-Evolution Contrast for Robust Depth Estimation
Jing Cao, Kui Jiang, Shenyi Li +2
Self-supervised depth estimation has gained significant attention in autonomous driving and robotics. However, existing methods exhibit substantial performance degradation under ad…
MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
Chuanyu Tang, Yilong Chen, Zhenyu Zhang +4
Low-Rank Adaptation (LoRA) drives research to align its performance with full fine-tuning. However, significant challenges remain: (1) Simply increasing the rank size of LoRA does…
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning
Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych +1
While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Ma…
Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge
Jiawei Liu, Zi Xiong, Yi Jiang +4
Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally requires large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in…