31 citations · 62 across the 19 of their papers we have counts for
10 papers · 1 filter
Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens
Yichi Zhang, Zhuo Chen, Lingbing Guo +2
Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works…
Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion
Zhiqiang Liu, Yichi Zhang, Mengshu Sun +2
Multi-modal knowledge graph completion (MMKGC) aims to discover missing facts in multi-modal knowledge graphs (MMKGs) by leveraging both structural relationships and diverse modali…
K-ON: Stacking Knowledge On the Head Layer of Large Language Model
Lingbing Guo, Yichi Zhang, Zhongpu Bo +5
Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next tok…
OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
Zhiqiang Liu, Chengtao Gan, Junjie Wang +5
Existing domain-specific Large Language Models (LLMs) are typically developed by fine-tuning general-purposed LLMs with large-scale domain-specific corpora. However, training on la…
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Yichi Zhang, Zhuo Chen, Lingbing Guo +8
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud h…
MKGL: Mastery of a Three-Word Language
Lingbing Guo, Zhongpu Bo, Zhuo Chen +10
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs),…
Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
Yichi Zhang, Binbin Hu, Zhuo Chen +6
Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train ne…
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
Xiaoze Liu, Feijie Wu, Tianyang Xu +4
The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern fo…
Noise-powered Multi-modal Knowledge Graph Representation Framework
Zhuo Chen, Yin Fang, Yichi Zhang +5
The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for…
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Yichi Zhang, Zhuo Chen, Yin Fang +4
Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especial…