SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
This addresses the need for safe and ethical AI by enabling the removal of sensitive information from LLMs, though it is incremental as it builds on existing unlearning research.
The paper introduced SemEval-2025 Task 4, which tackled the problem of unlearning sensitive content from Large Language Models through three subtasks involving synthetic and real documents, resulting in over 100 submissions from more than 30 institutions.
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.