10 citations · 18 across the 13 of their papers we have counts for
7 papers · 1 filter
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Yimin Deng, Yuxia Wu, Yejing Wang +9
Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on eith…
Training-free LLM Merging for Multi-task Learning
Zichuan Fu, Xian Wu, Yejing Wang +6
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen…
Sliding Window Attention Training for Efficient Large Language Models
Zichuan Fu, Wentao Song, Yejing Wang +7
Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity…
Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation
Derong Xu, Xinhang Li, Ziheng Zhang +7
Large Language Models (LLMs) demonstrate remarkable capabilities, yet struggle with hallucination and outdated knowledge when tasked with complex knowledge reasoning, resulting in…
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
Derong Xu, Ziheng Zhang, Zhihong Zhu +7
The impressive capabilities of large language models (LLMs) have attracted extensive interests of applying LLMs to medical field. However, the complex nature of clinical environmen…
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Derong Xu, Ziheng Zhang, Zhenxi Lin +6
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links. Description-based KGC leverages…
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
Derong Xu, Ziheng Zhang, Zhihong Zhu +9
Model editing aims to precisely alter the behaviors of large language models (LLMs) in relation to specific knowledge, while leaving unrelated knowledge intact. This approach has p…