arXiv:2605.22532v1 Announce Type: new Abstract: Linear properties are ubiquitous in the representations of language models; however, testing them experimentally remains a challenging task. This work focuses on relational linearity: the hypothesis that, for a fixed relation (e.g., "plays"), the unembedding of an object (e.g., "trumpet") can be predicted from the embedding of its subject (e.g.,"Miles Davis") by a linear map. We present an experimental method to test the formulation of relational linearity by Marconato et al. (2025). Specifically, we introduce a probing method, based on Kullback-
The rapid advancement of large language models necessitates deeper understanding of their internal representations to improve design and address limitations, making empirical investigations into linearity timely.
Understanding relational linearity offers a path to more interpretable, controllable, and potentially more efficient AI models, which is crucial for their deployment in critical applications.
This work provides a new experimental method to rigorously test a specific type of linearity in language models, potentially shifting how researchers debug and develop these complex systems.
- · AI researchers
- · AI developers
- · NLP applications
- · Black-box AI approaches
Improved understanding of language model internal workings.
Development of more robust and reliable AI systems with better predictability.
Accelerated progress towards explainable AI and more human-aligned artificial intelligence.
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