4 papers
ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
Xianming Li, Zongxi Li, Tsz-fung Andrew Lee +3
Parameter-efficient fine-tuning (PEFT) reduces the training cost of full-parameter fine-tuning for large language models (LLMs) by training only a small set of task-specific parame…
CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering
Zongxi Li, Yang Li, Haoran Xie +1
Users often assume that large language models (LLMs) share their cognitive alignment of context and intent, leading them to omit critical information in question-answering (QA) and…
MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation
Yufeng Jiang, Zongxi Li, Xiangyan Chen +2
Recent advancements in medical imaging have resulted in more complex and diverse images, with challenges such as high anatomical variability, blurred tissue boundaries, low organ c…
Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps
Fuxiao Liu, Paiheng Xu, Zongxia Li +2
We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs). Specifically, we explore the impacts of gr…