Linear Representations of Hierarchical Concepts in Language Models
We investigate how and to what extent hierarchical relations (e.g., Japan $\subset$ Eastern Asia $\subset$ Asia) are encoded in the internal representations of language models. Building on Linear Relational Concepts, we train linear transformations specific to each hierarchical depth and semantic domain, and characterize representational differences associated with hierarchical relations by comparing these transformations. Going beyond prior work on the representational geometry of hierarchies in LMs, our analysis covers multi-token entities and cross-layer representations. Across multiple domains we learn such transformations and evaluate in-domain generalization to unseen data and cross...
| Authors | Masaki Sakata, Benjamin Heinzerling, Takumi Ito et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 April 2026 |
| Last updated at source | 13 April 2026 |
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