Construction Grammar (CxG) knowledge in language models has been extensively studied for English, but remains underexplored in other languages. In Mandarin Chinese, the ba (把, disposal) and bei (被, passive) constructions are widely used for managing information structure. They foreground topical elements (information structure) and encode systematic form-meaning mappings (CxG), particularly with respect to the semantic role of the object. We probe language models’ linguistic competence with these constructions using minimal pairs, constructing a new minimal-pair dataset comprising seven paradigms that target both syntactic constraints and verb–construction compatibility. Our results show that it remains a challenge for many models to capture the form-meaning mappings underlying the ba construction, although they achieve high accuracy on paradigms driven by surface syntactic cues.

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.