Adding a single hyperbolic layer to a frozen T5 model with soft prompts improves 2-hop question answering exact match scores on four datasets compared with a matched Euclidean layer.
Multi-relational Poincar\'e Graph Embeddings
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abstract
Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues. However, multi-relational knowledge graphs often exhibit multiple simultaneous hierarchies, which current hyperbolic models do not capture. To address this, we propose a model that embeds multi-relational graph data in the Poincar\'e ball model of hyperbolic space. Our Multi-Relational Poincar\'e model (MuRP) learns relation-specific parameters to transform entity embeddings by M\"obius matrix-vector multiplication and M\"obius addition. Experiments on the hierarchical WN18RR knowledge graph show that our Poincar\'e embeddings outperform their Euclidean counterpart and existing embedding methods on the link prediction task, particularly at lower dimensionality.
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2025 1verdicts
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Multi-Hop Reasoning for Question Answering with Hyperbolic Representations
Adding a single hyperbolic layer to a frozen T5 model with soft prompts improves 2-hop question answering exact match scores on four datasets compared with a matched Euclidean layer.