MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
arXiv:2602.17088v1 Announce Type: new Abstract: The growing concern over training data privacy has elevated the "Right to be Forgotten" into a critical requirement, thereby raising the demand for effective Machine Unlearning. However, existing unlearning approaches commonly suffer from a fundamental trade-off: aggressively erasing the influence of target data often degrades model utility on retained data, while conservative strategies leave residual target information intact. In this work, the intrinsic representation properties learned during model pretraining are analyzed. It is demonstrated that semantic class concepts are entangled at the feature-pattern level, sharing associated features while preserving concept-specific discriminative components. This entanglement fundamentally limits the effectiveness of existing unlearning paradigms. Motivated by this insight, we propose Machine-Guided Unlearning (MeGU), a novel framework that guides unlearning through concept-aware re-alignme
arXiv:2602.17088v1 Announce Type: new Abstract: The growing concern over training data privacy has elevated the "Right to be Forgotten" into a critical requirement, thereby raising the demand for effective Machine Unlearning. However, existing unlearning approaches commonly suffer from a fundamental trade-off: aggressively erasing the influence of target data often degrades model utility on retained data, while conservative strategies leave residual target information intact. In this work, the intrinsic representation properties learned during model pretraining are analyzed. It is demonstrated that semantic class concepts are entangled at the feature-pattern level, sharing associated features while preserving concept-specific discriminative components. This entanglement fundamentally limits the effectiveness of existing unlearning paradigms. Motivated by this insight, we propose Machine-Guided Unlearning (MeGU), a novel framework that guides unlearning through concept-aware re-alignment. Specifically, Multi-modal Large Language Models (MLLMs) are leveraged to explicitly determine re-alignment directions for target samples by assigning semantically meaningful perturbing labels. To improve efficiency, inter-class conceptual similarities estimated by the MLLM are encoded into a lightweight transition matrix. Furthermore, MeGU introduces a positive-negative feature noise pair to explicitly disentangle target concept influence. During finetuning, the negative noise suppresses target-specific feature patterns, while the positive noise reinforces remaining associated features and aligns them with perturbing concepts. This coordinated design enables selective disruption of target-specific representations while preserving shared semantic structures. As a result, MeGU enables controlled and selective forgetting, effectively mitigating both under-unlearning and over-unlearning.
Executive Summary
The MeGU framework proposes a novel approach to machine unlearning by leveraging Multi-modal Large Language Models to guide the unlearning process through concept-aware re-alignment. This framework addresses the trade-off between aggressively erasing target data influence and conservatively retaining model utility on retained data. By introducing a positive-negative feature noise pair, MeGU enables selective disruption of target-specific representations while preserving shared semantic structures, effectively mitigating both under-unlearning and over-unlearning.
Key Points
- ▸ MeGU framework proposes a novel approach to machine unlearning
- ▸ Leverages Multi-modal Large Language Models for concept-aware re-alignment
- ▸ Introduces a positive-negative feature noise pair for selective disruption of target-specific representations
Merits
Effective Mitigation of Under- and Over-Unlearning
MeGU's design enables controlled and selective forgetting, mitigating the trade-off between aggressively erasing target data influence and conservatively retaining model utility on retained data.
Improved Efficiency
MeGU's use of inter-class conceptual similarities estimated by the MLLM and encoded into a lightweight transition matrix improves efficiency.
Demerits
Complexity of MLLM Integration
The integration of Multi-modal Large Language Models may add complexity to the MeGU framework, potentially limiting its applicability in certain scenarios.
Dependence on High-Quality Training Data
MeGU's effectiveness may depend on the quality of the training data, particularly in terms of the representation properties learned during model pretraining.
Expert Commentary
The MeGU framework represents a significant advancement in the field of machine unlearning, offering a novel approach to addressing the trade-off between data deletion and model utility. By leveraging the strengths of Multi-modal Large Language Models, MeGU enables selective disruption of target-specific representations while preserving shared semantic structures. However, the complexity of MLLM integration and dependence on high-quality training data may limit MeGU's applicability in certain scenarios. Further research is needed to fully explore the potential of MeGU and its implications for the development of more efficient and effective AI systems.
Recommendations
- ✓ Further research is needed to explore the potential of MeGU in various applications and scenarios.
- ✓ Developing clear guidelines and regulations for the implementation of data deletion and unlearning in AI systems is crucial for ensuring the effective protection of data privacy and the 'Right to be Forgotten'.
