This paper studies whether function vectors — task-specific latent directions derived from in-context demonstrations — transfer across languages for emotion detection, using multilingual multi-label emotion recognition as a testbed. Function vectors extracted from one language can steer model behavior in another language without inference-time demonstrations, substantially improving cross-lingual performance with less compute overhead than standard few-shot in-context learning.
