PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning

• URL: http://arxiv.org/abs/2507.01271v1
• Date: Wed, 02 Jul 2025 01:13:08 GMT
• Title: PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning
• Authors: Tatsuki Kawakami, Kazuki Egashira, Atsuyuki Miyai, Go Irie, Kiyoharu Aizawa,
• Abstract summary: We introduce PULSE protocol for realistic unlearning scenarios for LMMs.<n>We then evaluate existing unlearning methods along these dimensions.<n>Our results reveal that, although some techniques can successfully unlearn knowledge acquired through fine-tuning, they struggle to eliminate information learned during pre-training.
• Score: 27.16106173526184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, unlearning techniques, which are methods for inducing a model to "forget" previously learned information, have attracted attention as a way to address privacy and copyright concerns in large language models (LLMs) and large multimodal models (LMMs). While several unlearning benchmarks have been established for LLMs, a practical evaluation framework for unlearning in LMMs has been less explored. Specifically, existing unlearning benchmark for LMMs considers only scenarios in which the model is required to unlearn fine-tuned knowledge through a single unlearning operation. In this study, we introduce PULSE protocol for realistic unlearning scenarios for LMMs by introducing two critical perspectives: (i) Pre-trained knowledge Unlearning for analyzing the effect across different knowledge acquisition phases and (ii) Long-term Sustainability Evaluation to address sequential requests. We then evaluate existing unlearning methods along these dimensions. Our results reveal that, although some techniques can successfully unlearn knowledge acquired through fine-tuning, they struggle to eliminate information learned during pre-training. Moreover, methods that effectively unlearn a batch of target data in a single operation exhibit substantial performance degradation when the same data are split and unlearned sequentially.

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