Lacuna Inc. at SemEval-2025 Task 4: LoRA-Enhanced Influence-Based Unlearning for LLMs
This addresses the need for efficient unlearning of sensitive content in LLMs, though it appears incremental as it builds on existing influence-based methods.
The paper tackled the problem of removing specific knowledge from large language models without full retraining, proposing LIBU, which combines influence functions and second-order optimization to achieve lightweight unlearning while maintaining utility.
This paper describes LIBU (LoRA enhanced influence-based unlearning), an algorithm to solve the task of unlearning - removing specific knowledge from a large language model without retraining from scratch and compromising its overall utility (SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models). The algorithm combines classical \textit{influence functions} to remove the influence of the data from the model and \textit{second-order optimization} to stabilize the overall utility. Our experiments show that this lightweight approach is well applicable for unlearning LLMs in different kinds of task.