H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models
Representing and navigating hierarchy is a fundamental primitive of reasoning. Large language models have demonstrated proficiency in a wide variety of tasks requiring hierarchical reasoning, but there exists limited analysis on how the models geometrically represent the necessary latent constructions for such thinking. To this end, we develop H-probes, a collection of linear probes that extract hierarchical structure, specifically depth and pairwise distance, from latent representations. In synthetic tree traversal tasks, the H-probes robustly find the subspaces containing hierarchical structure necessary to complete the tasks; furthermore, in comprehensive ablation experiments, we show ...
| Authors | Cutter Dawes, Aryan Sharma, Angelos Ioannis Lagos et al. |
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| Primary category | cs.CL |
| Published | 15 April 2026 |
| Last updated at source | 6 May 2026 |
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