Lacuna Inc. at SemEval-2025 Task 4: LoRA-Enhanced Influence-Based Unlearning for LLMs

• URL: http://arxiv.org/abs/2506.04044v1
• Date: Wed, 04 Jun 2025 15:10:09 GMT
• Title: Lacuna Inc. at SemEval-2025 Task 4: LoRA-Enhanced Influence-Based Unlearning for LLMs
• Authors: Aleksey Kudelya, Alexander Shirnin,
• Abstract summary: This paper describes LIBU (LoRA enhanced influence-based unlearning), an algorithm to solve the task of unlearning.<n>The algorithm combines classical textitinfluence functions to remove the influence of the data from the model and textitsecond-order optimization to stabilize the overall utility.
• Score: 49.1574468325115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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