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LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning

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arxiv 2406.01032 v1 pith:E7CL5GGO submitted 2024-06-03 cs.LG cs.AI

Junjie Xu , Zongyu Wu , Minhua Lin , Xiang Zhang , Suhang Wang This is my paper · ORCID
classification cs.LGcs.AI
keywords gnnsgraphmoleculardatamodelmultimodaldistillingframework
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Recent progress in Graph Neural Networks (GNNs) has greatly enhanced the ability to model complex molecular structures for predicting properties. Nevertheless, molecular data encompasses more than just graph structures, including textual and visual information that GNNs do not handle well. To bridge this gap, we present an innovative framework that utilizes multimodal molecular data to extract insights from Large Language Models (LLMs). We introduce GALLON (Graph Learning from Large Language Model Distillation), a framework that synergizes the capabilities of LLMs and GNNs by distilling multimodal knowledge into a unified Multilayer Perceptron (MLP). This method integrates the rich textual and visual data of molecules with the structural analysis power of GNNs. Extensive experiments reveal that our distilled MLP model notably improves the accuracy and efficiency of molecular property predictions.

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Cited by 5 Pith papers

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