{
  "annotation_id": "bed48d50-40a5-4351-a0d6-adec5dc22dd9",
  "date_created": "2026-02-17T05:53:08.615000Z",
  "date_modified": "2026-02-17T05:53:08.615000Z",
  "file_hash": "9ae25e30a2df0849ce4a7f91fdeccb12a5871e62308201bbc49b2f199a4b5037",
  "private": false,
  "record": {
    "abstract": "We present the first comprehensive evaluation of cross-lingual unlearning in multilingual LLMs. Using translated TOFU benchmarks in seven language/script variants, we test major unlearning algorithms and show that most fail to remove facts outside the training language, even when utility remains high. However, subspace-projection consistently outperforms the other methods, achieving strong cross-lingual forgetting with minimal degradation. Analysis of learned task subspaces reveals a shared interlingua structure: removing this shared subspace harms all languages, while removing language-specific components selectively affects one. These results demonstrate that multilingual forgetting depends on geometry in weight space, motivating subspace-based approaches for future unlearning systems.",
    "arxiv_id": "2601.06675",
    "authors": [
      "Tyler Lizzo",
      "Larry Heck"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Evaluating Cross-Lingual Unlearning in Multilingual Language Models",
    "url": "https://arxiv.org/abs/2601.06675",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "894127c2-b189-4f30-9a63-0b9620cbeef9",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}