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    <abstract>As large language models (LLM) trained on massive corpora scraped from the web exhibit the capability to reproduce sensitive and copyright-protected data, the field of machine unlearning has emerged to address the arising ethical and legal concerns.While previous research has provided a unified evaluation of LLM unlearning methods, this unification remains constrained to English-only models and datasets.We aim to address the prevailing fragmentation in recent cross-lingual unlearning research by extending existing unified benchmarks with multilingual data.To that end, we plan to compile a dataset of parallel translations of question-answer pairs consisting of real-world facts and synthetic personally identifiable information.Moreover, we will focus on mitigating model degradation during unlearning by selectively editing only those layers that contain the given knowledge.</abstract>
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%0 Conference Proceedings
%T Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models
%A Bronec, Jan
%A Helcl, Jindřich
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F bronec-helcl-2026-thesis
%X As large language models (LLM) trained on massive corpora scraped from the web exhibit the capability to reproduce sensitive and copyright-protected data, the field of machine unlearning has emerged to address the arising ethical and legal concerns.While previous research has provided a unified evaluation of LLM unlearning methods, this unification remains constrained to English-only models and datasets.We aim to address the prevailing fragmentation in recent cross-lingual unlearning research by extending existing unified benchmarks with multilingual data.To that end, we plan to compile a dataset of parallel translations of question-answer pairs consisting of real-world facts and synthetic personally identifiable information.Moreover, we will focus on mitigating model degradation during unlearning by selectively editing only those layers that contain the given knowledge.
%U https://aclanthology.org/2026.acl-srw.49/
%P 554-562

Markdown (Informal)

[Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models](https://aclanthology.org/2026.acl-srw.49/) (Bronec & Helcl, ACL 2026)

• Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models (Bronec & Helcl, ACL 2026)

ACL

• Jan Bronec and Jindřich Helcl. 2026. Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 554–562, San Diego, California, United States. Association for Computational Linguistics.