Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
arXiv:2606.10338v1 Announce Type: new Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored. Unlike dense models, MoE architectures employ a router at each layer to assign each token to a sparse subset of experts. In this work, we observe that forget data often activates a small subset of experts disproportionately, while these experts may receive much weaker activation from retain data. This forget--retain routing mismatch can leave forget-critical experts under-regularized during unlear
The increasing focus on data privacy, regulatory compliance (e.g., GDPR), and the productionisation of large language models necessitates robust machine unlearning capabilities.
Sophisticated unlearning methods for MoE models are crucial for mitigating biases, complying with data privacy laws, and refining model behavior post-deployment without retraining from scratch.
The ability to selectively remove specific learned information from complex MoE architectures becomes more practical and efficient, enhancing model governance and adaptability.
- · AI developers
- · Cloud providers
- · Regulated industries
- · Data privacy advocates
- · Ad-hoc unlearning methods
- · Models with unmitigated biases
Improved model privacy and compliance for large language models, especially those used in sensitive applications.
Faster iteration cycles and reduced operational costs for large AI model maintenance due to more efficient data removal.
Enhanced public trust in AI systems through demonstrable and auditable mechanisms for data forgetting and bias correction.
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