← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-srw.49 · 记录 #1
Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models
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.
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