Papers by Yihua Zhu
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
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Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding (2024.findings-emnlp)
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| Challenge: | Existing knowledge graph embeddings (KGs) are limited in their flexibility and difficulties in generalizing them for higher-dimensional rotations. |
| Approach: | They propose a KGE model employing matrices for entities and block-diagonal orthogonal matrics with Riemannian optimization for relations that captures several relation patterns that rotation-based methods can identify. |
| Outcome: | The proposed model outperforms state-of-the-art models while reducing the number of relation parameters. |
3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding (2024.eacl-long)
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| Challenge: | Existing knowledge graph embeddings do not capture relation patterns, but they capture symmetry, antisymmetry, inversion, commutative composition, non-commutable composition, hierarchy, and multiplicity. |
| Approach: | They propose a 3D Rotation and Translation in Hyperbolic space model that captures relation patterns simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of accuracy, hierarchy property, and other relation patterns in low-dimensional space, while performing similarly in high-dimensional spaces. |