@inproceedings{ramakrishna-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 4: Unlearning sensitive content from Large Language Models",
    author = "Ramakrishna, Anil  and
      Wan, Yixin  and
      Jin, Xiaomeng  and
      Chang, Kai - Wei  and
      Bu, Zhiqi  and
      Vinzamuri, Bhanukiran  and
      Volkan Cevher, Volkan  and
      Hong, Mingyi  and
      Gupta, Rahul",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.329/",
    pages = "2584--2596",
    ISBN = "979-8-89176-273-2",
    abstract = "We introduce SemEval-2025 Task 4: unlearn- ing 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) un- learn short form synthetic biographies contain- ing personally identifiable information (PII), in- cluding fake names, phone number, SSN, email and home addresses, and (3) unlearn real docu- ments 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."
}