Function vectors for translation tasks are largely language-agnostic—vectors learned from one language pair can improve translation to other languages, suggesting task representations in multilingual models transcend specific language pairs.
Function vectors are task representations extracted from language models during in-context learning. This paper investigates whether these vectors work across languages, using machine translation as a test case.