Unlearning aims to remove copyrighted, sensitive, or private content from large language models (LLMs) without a full retraining. In this work, we develop a multi-task unlearning benchmark LUME that features three tasks: (1) unlearn synthetically generated creative short novels, (2) unlearn synthetic biographies with sensitive information, and (3) unlearn a collection of public biographies. We further release two fine-tuned LLMs of 1B and 7B parameter sizes as the target models. We conduct detailed evaluations of several recently-proposed algorithms and present results on carefully crafted metrics to understand their behavior and limitations.

Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspective, framing it as a regularized multi-task optimization problem, where one task optimizes a forgetting objective and another optimizes the model performance. In particular, we introduce a normalized gradient difference algorithm, enabling us to have better control over the trade-off between the objectives, while integrating a new, automatic learning rate scheduler. We provide a theoretical analysis and empirically demonstrate the superior performance of among state-of-the-art unlearning methods on the TOFU and MUSE datasets while exhibiting stable training.

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.