BriefGPT.xyz
May, 2024
用于图指导调优的联合嵌入
Joint Embeddings for Graph Instruction Tuning
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Vlad Argatu, Aaron Haag, Oliver Lohse
TL;DR
该论文研究了将图模态集成到大型语言模型中,以提升其在图解指令任务中的性能表现,并通过图嵌入训练模型,使其能够理解和基于图表示生成回答。该方法在性能上显著优于图文方法,并且对于较大的图结构保持一致。
Abstract
large language models
(LLMs) have achieved impressive performance in text understanding and have become an essential tool for building smart assistants. Originally focusing on text, they have been enhanced with
multimod
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