3 citations · 6 across the 8 of their papers we have counts for
8 papers
PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning
Hangrui Zhang, Feifei Shao, Yawei Luo +6
In-context learning (ICL) has attracted increasing attention for enabling models to perform new tasks using only a few ``input--output'' prompt examples. However, existing approach…
Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
Songtao Jiang, Yuan Wang, Sibo Song +22
Real-world clinical decision-making requires integrating heterogeneous data, including medical text, 2D images, 3D volumes, and videos, while existing AI systems fail to unify all…
MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing
Feifei Shao, Ping Liu, Zhao Wang +3
Point cloud processing (PCP) encompasses tasks like reconstruction, denoising, registration, and segmentation, each often requiring specialized models to address unique task charac…
DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models
Jiabao Pan, Yan Zhang, Chen Zhang +3
Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast C…
Retrieval Augmented Instruction Tuning for Open NER with Large Language Models
Tingyu Xie, Jian Zhang, Yan Zhang +3
The strong capability of large language models (LLMs) has been applied to information extraction (IE) through either retrieval augmented prompting or instruction tuning (IT). Howev…
Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task
Shurong Wang, Yufei Zhang, Xuliang Huang +1
Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models…
UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts
Chenlu Zhan, Yufei Zhang, Yu Lin +2
Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-growing medical diagnostics application. However, most Med-VLP models learn task-specific r…
Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
Tingyu Xie, Qi Li, Yan Zhang +2
Exploring the application of powerful large language models (LLMs) on the named entity recognition (NER) task has drawn much attention recently. This work pushes the performance bo…