Relational Linear Properties in Language Models: An Empirical Investigation

2026-05-21 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

An empirical investigation explores relational linearity in language models, a hypothesis stating that for a fixed relation, an object's unembedding can be linearly predicted from its subject's embedding. This work, building on Marconato et al. (2025), introduces an efficient probing method based on Kullback-Leibler divergence to test this property. This new approach avoids crude Jacobian approximations used in prior methods like Linear Relational Embeddings by Hernandez et al. (2024). Across four datasets, the study reveals that relational linearity varies significantly among different models and exhibits distinct layer-wise patterns, aligning with previous observations on linguistic information in model representations. Furthermore, the research indicates that changes in how a relation is phrased differentially impact relational linearity.

Key takeaway

For AI Scientists investigating language model representations, understanding relational linearity is crucial. This research demonstrates that the choice of model and how relations are phrased significantly influences these linear properties. You should consider employing Kullback-Leibler divergence-based probing for efficient and accurate evaluation of relational linearity, especially when analyzing linguistic information across model layers. This can refine your understanding of how knowledge is encoded and accessed within different architectures.

Key insights

A new Kullback-Leibler divergence probing method efficiently tests relational linearity in language models, showing variations across models and sensitivity to phrasing.

Principles

• Relational linearity varies across models.
• Linguistic information shows layer-wise patterns.
• Relation phrasing impacts linearity.

Method

A Kullback-Leibler divergence probing method evaluates relational linearity, efficiently avoiding Jacobian approximations used in previous Linear Relational Embeddings.

Topics

• Relational Linearity
• Language Model Representations
• Probing Methods
• Kullback-Leibler Divergence
• Linguistic Information

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.