5 citations · 10 across the 6 of their papers we have counts for
6 papers
ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
Xianming Li, Zongxi Li, Tsz-fung Andrew Lee +3
Popular low-rank parameter-efficient fine-tuning (PEFT) methods represent adaptation as separate updates to selected backbone weights, without maintaining an explicit task-specific…
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…
Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
Xinhong Chen, Zongxi Li, Yaowei Wang +3
The study of causal relationships between emotions and causes in texts has recently received much attention. Most works focus on extracting causally related clauses from documents.…
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…
Recurrent Attention Networks for Long-text Modeling
Xianming Li, Zongxi Li, Xiaotian Luo +5
Self-attention-based models have achieved remarkable progress in short-text mining. However, the quadratic computational complexities restrict their application in long text proces…