Summary of Graph Language Models, by Moritz Plenz et al.
Graph Language Models
by Moritz Plenz, Anette Frank
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents a novel approach to integrating language models (LMs) with structured knowledge graphs (KGs). The Graph Language Model (GLM) combines the strengths of both LMs and Graph Neural Networks (GNNs), mitigating their weaknesses. The GLM is initialized from a pretrained LM, enabling it to understand individual graph concepts and triplets. Its architecture incorporates graph biases, promoting effective knowledge distribution within the graph. This allows GLMs to process graphs, texts, and interleaved inputs of both. Empirical evaluations on relation classification tasks show that GLM embeddings surpass both LM- and GNN-based baselines in supervised and zero-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to understand language and structured information together. It makes a special kind of AI model called the Graph Language Model (GLM) that combines two other approaches: language models and graph neural networks. The GLM starts with knowledge from a pre-trained language model, then adds features that help it understand structured information like graphs. This lets the GLM process both language and structure together. Tests show that this new approach works better than older methods for understanding relationships. |
Keywords
* Artificial intelligence * Classification * Gnn * Language model * Supervised * Zero shot
