SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913775575105536 |
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| author | Ramakrishna, Anil Wan, Yixin Jin, Xiaomeng Chang, Kai-Wei Bu, Zhiqi Vinzamuri, Bhanukiran Cevher, Volkan Hong, Mingyi Gupta, Rahul |
| author_facet | Ramakrishna, Anil Wan, Yixin Jin, Xiaomeng Chang, Kai-Wei Bu, Zhiqi Vinzamuri, Bhanukiran Cevher, Volkan Hong, Mingyi Gupta, Rahul |
| contents | We introduce SemEval-2025 Task 4: unlearning sensitive content from Large Language Models (LLMs). The task features 3 subtasks for LLM unlearning spanning different use cases: (1) unlearn long form synthetic creative documents spanning different genres; (2) unlearn short form synthetic biographies containing personally identifiable information (PII), including fake names, phone number, SSN, email and home addresses, and (3) unlearn real documents sampled from the target model's training dataset. We received over 100 submissions from over 30 institutions and we summarize the key techniques and lessons in this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models Ramakrishna, Anil Wan, Yixin Jin, Xiaomeng Chang, Kai-Wei Bu, Zhiqi Vinzamuri, Bhanukiran Cevher, Volkan Hong, Mingyi Gupta, Rahul Computation and Language Machine Learning We introduce SemEval-2025 Task 4: unlearning sensitive content from Large Language Models (LLMs). The task features 3 subtasks for LLM unlearning spanning different use cases: (1) unlearn long form synthetic creative documents spanning different genres; (2) unlearn short form synthetic biographies containing personally identifiable information (PII), including fake names, phone number, SSN, email and home addresses, and (3) unlearn real documents sampled from the target model's training dataset. We received over 100 submissions from over 30 institutions and we summarize the key techniques and lessons in this paper. |
| title | SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.02883 |