Summary of Multi-view Empowered Structural Graph Wordification For Language Models, by Zipeng Liu et al.
Multi-View Empowered Structural Graph Wordification for Language Models
by Zipeng Liu, Likang Wu, Ming He, Zhong Guan, Hongke Zhao, Nan Feng
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A novel framework for Large Language Models (LLMs) to effectively integrate with graph-structured data is introduced. The Dual-Residual Vector Quantized-Variational AutoEncoder, or Dr.E, enables end-to-end modality-aligning between LLMs and graphs by facilitating token-level alignment. This approach enhances the structural understanding of graphs in LLMs through incorporating multiple views of central nodes based on their surrounding nodes at various distances. The framework demonstrates competitive performance with state-of-the-art approaches on standard graph tasks while providing visual interpretability, efficiency, and robustness. The code is available at https://github.com/Timothy914/Dr.E. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to connect powerful language models with complex data is being developed. This technology helps large language models work better with graphs, which are special kinds of data that have connections between things. The method uses a combination of ideas from computer vision and natural language processing to align the language models with the graph data. This approach shows promise in improving the understanding of graph structures by language models. It also performs well compared to other methods on standard tests. The code for this project is available online. |
Keywords
» Artificial intelligence » Alignment » Natural language processing » Token » Variational autoencoder
