A do-intervention on a LEACE subspace steers number but not gender

Guerner et al. propose a causal graphical model where a latent concept and context jointly generate the hidden state, and formalize a genuine do-intervention on the LEACE-found concept subspace [guerner-etal-2023-geometric-causal-probing] On GPT-2-large, setting verbal number to plural raises the plural verb form's probability in ~90% of contexts, a causally-grounded controlled-generation result [guerner-etal-2023-geometric-causal-probing] The same intervention on grammatical gender in a French GPT-2 fails: setting masculine reduces accuracy and setting feminine has no significant effect [guerner-etal-2023-geometric-causal-probing] Their Theorem 4.1 proves a subspace meeting four intrinsic criteria (erasure, encapsulation, containment, stability) guarantees such interventions succeed, tying the number/gender asymmetry to how completely LEACE captures each concept [guerner-etal-2023-geometric-causal-probing]