@inproceedings{farashah-etal-2026-multilingual,
title = "Multilingual Amnesia: On the Transferability of Unlearning in Multilingual {LLM}s",
author = "Farashah, Alireza Dehghanpour and
Khandelwal, Aditi and
Fauchard, Marylou and
Shi, Zhuan and
Rostamzadeh, Negar and
Farnadi, Golnoosh",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.260/",
pages = "5570--5589",
ISBN = "979-8-89176-380-7",
abstract = "As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing research on machine unlearning has mainly focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we address the problem of multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes into ten languages through translation{---}English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian{---}spanning five language families and varying resource levels. Our experiments show that unlearning in high-resource languages tends to be more stable, with asymmetric transfer observed between typologically related languages. Moreover, analysis of linguistic distances reveals that syntactic similarity is the most predictive factor of cross-lingual unlearning effects."
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<abstract>As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing research on machine unlearning has mainly focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we address the problem of multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes into ten languages through translation—English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian—spanning five language families and varying resource levels. Our experiments show that unlearning in high-resource languages tends to be more stable, with asymmetric transfer observed between typologically related languages. Moreover, analysis of linguistic distances reveals that syntactic similarity is the most predictive factor of cross-lingual unlearning effects.</abstract>
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%0 Conference Proceedings
%T Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs
%A Farashah, Alireza Dehghanpour
%A Khandelwal, Aditi
%A Fauchard, Marylou
%A Shi, Zhuan
%A Rostamzadeh, Negar
%A Farnadi, Golnoosh
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F farashah-etal-2026-multilingual
%X As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing research on machine unlearning has mainly focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we address the problem of multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes into ten languages through translation—English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian—spanning five language families and varying resource levels. Our experiments show that unlearning in high-resource languages tends to be more stable, with asymmetric transfer observed between typologically related languages. Moreover, analysis of linguistic distances reveals that syntactic similarity is the most predictive factor of cross-lingual unlearning effects.
%U https://aclanthology.org/2026.eacl-long.260/
%P 5570-5589
Markdown (Informal)
[Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs](https://aclanthology.org/2026.eacl-long.260/) (Farashah et al., EACL 2026)
ACL
- Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard, Zhuan Shi, Negar Rostamzadeh, and Golnoosh Farnadi. 2026. Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5570–5589, Rabat, Morocco. Association for Computational Linguistics.