6 papers · 1 filter
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
Muzhi Li, Cehao Yang, Chengjin Xu +5
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vu…
ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
Zhengzhuo Xu, Bowen Qu, Yiyan Qi +4
Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart…
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Muzhi Li, Cehao Yang, Chengjin Xu +5
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as…
MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
Zhanpeng Chen, Chengjin Xu, Yiyan Qi +1
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities. However, a significant draw…
Context Graph
Chengjin Xu, Muzhi Li, Cehao Yang +4
Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples. However, triple-based KGs lack the conte…
A Survey on Large Language Model Hallucination via a Creativity Perspective
Xuhui Jiang, Yuxing Tian, Fengrui Hua +3
Hallucinations in large language models (LLMs) are always seen as limitations. However, could they also be a source of creativity? This survey explores this possibility, suggesting…