Summary of Sempool: Simple, Robust, and Interpretable Kg Pooling For Enhancing Language Models, by Costas Mavromatis et al.
SemPool: Simple, robust, and interpretable KG pooling for enhancing language models
by Costas Mavromatis, Petros Karypis, George Karypis
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 A novel approach to knowledge graph (KG) powered question answering (QA) has been proposed, which leverages graph neural networks (GNNs) and language models (LMs) for effective reasoning. The method, termed SemPool, learns to aggregate semantic information from the underlying KG and fuses it with LMs at different layers, enabling QA in challenging settings where critical answer information is missing from the KG. Experimental results show that SemPool outperforms state-of-the-art GNN-based methods by 2.27% accuracy points on average, offering interpretability on what type of graph information is fused. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to answer questions using knowledge graphs has been discovered! It uses special networks called Graph Neural Networks and language models to understand complex relationships between words and facts. This method, called SemPool, can even work well when some important answers are missing from the knowledge graph. The results show that it does a better job than other methods by 2.27% points on average. Plus, it helps us understand what kind of information is being used to answer questions. |
Keywords
» Artificial intelligence » Gnn » Knowledge graph » Question answering
