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The Geometry of Metaphor: Probing and Steering Metaphor Representations in Large Language Models

2026-10-22 16:33:01
Shihui Li, Mette Andersen, Martha Lewis
arXiv_CL
         
Abstract

Metaphor is a crucial aspect of human cognition. As such, we would like to understand how it is represented in large language models (LLMs) and how it might be controlled. We use mechanistic interpretability techniques to identify causal subspaces in a range of LLMs covering different architectures and sizes. We show that we are able to steer output on classification tasks across different parts of speech. We are also able to steer output on generation tasks, indicating that these techniques can be used to control the metaphoricity of LLM output.

Abstract (translated)

URL

https://arxiv.org/abs/2610.31520

PDF

https://arxiv.org/pdf/2610.31520.pdf

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