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pro vyhledávání: '"graph language"'
Autor:
Kong, Lecheng, Feng, Jiarui, Liu, Hao, Huang, Chengsong, Huang, Jiaxin, Chen, Yixin, Zhang, Muhan
Foundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text and image data, graph data do not have a definitive structure, posing
Externí odkaz:
http://arxiv.org/abs/2407.09709
Recently there has been a surge of interest in extending the success of large language models (LLMs) to graph modality, such as social networks and molecules. As LLMs are predominantly trained with 1D text data, most existing approaches adopt a graph
Externí odkaz:
http://arxiv.org/abs/2406.14021
Autor:
Plenz, Moritz, Frank, Anette
While Language Models (LMs) are the workhorses of NLP, their interplay with structured knowledge graphs (KGs) is still actively researched. Current methods for encoding such graphs typically either (i) linearize them for embedding with LMs -- which u
Externí odkaz:
http://arxiv.org/abs/2401.07105
Autor:
de Oliveira, Wallyson Lemes, Shamsaddini, Vahid, Ghofrani, Ali, Inda, Rahul Singh, Veeramaneni, Jithendra Sai, Voutaz, Étienne
This scientific report presents a novel methodology for the early prediction of important political events using News datasets. The methodology leverages natural language processing, graph theory, clique analysis, and semantic relationships to uncove
Externí odkaz:
http://arxiv.org/abs/2403.17816
Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges. Recent advancements in heterogeneous graph neural ne
Externí odkaz:
http://arxiv.org/abs/2402.16024
Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged -- transformer-based models inspired by LLMs, and Graph N
Externí odkaz:
http://arxiv.org/abs/2401.02823
Autor:
Liu, Zhiyuan, Li, Sihang, Luo, Yanchen, Fei, Hao, Cao, Yixin, Kawaguchi, Kenji, Wang, Xiang, Chua, Tat-Seng
Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception - a critical ability of human professionals in comprehending molecules' topological
Externí odkaz:
http://arxiv.org/abs/2310.12798
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