Cross-Lingual Steering for Figurative Language Generation
cs.CL
Submitted: 2026-05-28
Updated: 2026-10-13
Comments: 22 pages, 4 figures
Code: https://github.com/DUTIR-Emotion-Group/CCL2018-Chinese-Metaphor-Analysis
License: http://creativecommons.org/licenses/by/4.0/
The gist: Cross-lingual activation steering has been studied mainly for language-agnostic properties such as factual knowledge and reasoning.
Terminology
Abstract
Cross-lingual activation steering has been studied mainly for language-agnostic properties such as factual knowledge and reasoning. Its transferability remains unclear for figurative language, whose expressions are often tied to a particular language. In this work, we study cross-lingual steering of figurative language: whether steering vectors learned in one language can affect generation in another. We first show that within a single language, steering effectively increases the ratio of the intended figure of speech, especially for metaphor and idiom. Then under the cross-lingual setting, across 408 comparisons spanning four multilingual models, six languages, and four figures of speech, steering increases the figurative ratio from the unsteered 12.1% to 20.4%. To understand the mechanism, we decompose each language's own steering vector into the mean of steering vectors from other languages and a residual, and show that this mean direction matches or outperforms the language's own vector while the residual steers worse. This suggests that, despite the highly language-dependent nature of figurative expressions, a partially shared direction exists in the representation space that enables figurative knowledge to transfer across languages.
Sources
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- The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
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