156 citations · 243 across the 8 of their papers we have counts for
9 papers
UMIE: Unified Multimodal Information Extraction with Instruction Tuning
Lin Sun, Kai Zhang, Qingyuan Li +1
Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-speci…
Multimodal Question Answering for Unified Information Extraction
Yuxuan Sun, Kai Zhang, Yu Su
Multimodal information extraction (MIE) aims to extract structured information from unstructured multimedia content. Due to the diversity of tasks and settings, most current MIE mo…
LKPNR: LLM and KG for Personalized News Recommendation Framework
Chen hao, Xie Runfeng, Cui Xiangyang +4
Accurately recommending candidate news articles to users is a basic challenge faced by personalized news recommendation systems. Traditional methods are usually difficult to grasp…
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
Kai Zhang, Bernal Jiménez Gutiérrez, Yu Su
Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP ta…
Backdoor Attacks to Pre-trained Unified Foundation Models
Zenghui Yuan, Yixin Liu, Kai Zhang +2
The rise of pre-trained unified foundation models breaks down the barriers between different modalities and tasks, providing comprehensive support to users with unified architectur…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…