From the 1 of 7 linked papers with an AI index.
7 papers
OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation
Yajing Xu, Yarong Lan, Jiaoyan Chen +6
The paper presents OmniPhys, a knowledge-graph-based benchmark for evaluating physical commonsense in text-to-image models, and OmniPrompt, an iterative optimization framework that…
Abstractive Visual Understanding of Multi-modal Structured Knowledge: A New Perspective for MLLM Evaluation
Yichi Zhang, Zhuo Chen, Lingbing Guo +4
Multi-modal large language models (MLLMs) incorporate heterogeneous modalities into LLMs, enabling a comprehensive understanding of diverse scenarios and objects. Despite the proli…
Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts
Yajing Xu, Zhiqiang Liu, Jiaoyan Chen +7
Multi-modal Knowledge Graphs (MMKGs) have been widely applied across various domains for knowledge representation. However, the existing MMKGs are significantly fewer than required…
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks withi…
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…
Is Large Language Model Good at Triple Set Prediction? An Empirical Study
Yuan Yuan, Yajing Xu, Wen Zhang
The core of the Knowledge Graph Completion (KGC) task is to predict and complete the missing relations or nodes in a KG. Common KGC tasks are mostly about inferring unknown element…