March 13, 2024, 4:47 a.m. | Atharva Phatak, Vijay K. Mago, Ameeta Agrawal, Aravind Inbasekaran, Philippe J. Giabbanelli

cs.CL updates on

arXiv:2403.07118v1 Announce Type: new
Abstract: The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability of large pretrained language models to generate text from causal graphs, where salient concepts are represented as nodes and causality is represented via directed, typed edges. The causal reasoning encoded in these graphs can support applications as diverse as healthcare or marketing. Using two publicly available …

abstract arxiv capability causal concepts explore facts generate generative graphs knowledge knowledge graphs language language models large language large language models nodes text type work

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