@inproceedings{laiyk-etal-2026-exploring,
title = "Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation",
author = "Laiyk, Nurkhan and
G{\'a}llego, Gerard I. and
Ferrando, Javier and
Koto, Fajri",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1126/",
pages = "23840--23851",
ISBN = "979-8-89176-462-0",
abstract = "Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that multilingual model representations can be language-agnostic, it remains unclear whether the same holds for function vectors. We study whether FVs exhibit language-agnosticity, using word-level machine translation as a case study. Across three decoder-only multilingual LLMs, we find that translation FVs extracted from word-to-word prompts in a single English$\to$X direction transfer to other target languages, consistently improving the rank of correct translation tokens across multiple unseen languages. We further find that the highest-gain tokens span multiple languages and that translation FVs across directions share most of their top-ranked heads, indicating that the FV encodes a largely language-agnostic translation signal."
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<abstract>Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that multilingual model representations can be language-agnostic, it remains unclear whether the same holds for function vectors. We study whether FVs exhibit language-agnosticity, using word-level machine translation as a case study. Across three decoder-only multilingual LLMs, we find that translation FVs extracted from word-to-word prompts in a single EnglishX direction transfer to other target languages, consistently improving the rank of correct translation tokens across multiple unseen languages. We further find that the highest-gain tokens span multiple languages and that translation FVs across directions share most of their top-ranked heads, indicating that the FV encodes a largely language-agnostic translation signal.</abstract>
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%0 Conference Proceedings
%T Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation
%A Laiyk, Nurkhan
%A Gállego, Gerard I.
%A Ferrando, Javier
%A Koto, Fajri
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F laiyk-etal-2026-exploring
%X Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that multilingual model representations can be language-agnostic, it remains unclear whether the same holds for function vectors. We study whether FVs exhibit language-agnosticity, using word-level machine translation as a case study. Across three decoder-only multilingual LLMs, we find that translation FVs extracted from word-to-word prompts in a single EnglishX direction transfer to other target languages, consistently improving the rank of correct translation tokens across multiple unseen languages. We further find that the highest-gain tokens span multiple languages and that translation FVs across directions share most of their top-ranked heads, indicating that the FV encodes a largely language-agnostic translation signal.
%U https://aclanthology.org/2026.findings-emnlp.1126/
%P 23840-23851
Markdown (Informal)
[Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation](https://aclanthology.org/2026.findings-emnlp.1126/) (Laiyk et al., Findings 2026)
ACL