Summary of Llm and Gnn Are Complementary: Distilling Llm For Multimodal Graph Learning, by Junjie Xu et al.
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
by Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, Suhang Wang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Recent advancements in Graph Neural Networks (GNNs) have significantly enhanced the capacity to model complex molecular structures for predicting properties. However, 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). Our proposed framework, GALLON (Graph Learning from Large Language Model Distillation), 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Scientists have made big progress in understanding how molecules work by using special computer programs called Graph Neural Networks (GNNs). But there’s more to molecules than just their shape – they also contain words and pictures. To make the most of all this information, we developed a new way to combine different types of data. Our method uses powerful language models and GNNs together to create a single tool that can analyze molecules better. We tested our approach and found it works much better than previous methods. |
Keywords
» Artificial intelligence » Distillation » Large language model
