arXiv:2606.26629v1 Announce Type: new Abstract: Weight-space regularization methods such as Elastic Weight Consolidation (EWC) are the standard approach to catastrophic forgetting in continual learning. However, those methods tend to underperform when applied to large language models. We argue that such underperformance can be partly explained by the ``polysemantic'' nature of large language models: per-weight importance estimates utilized by EWC-style regularization are too coarse and cannot isolate the knowledge that needs protection. In this paper, we propose regularizing instead in the mod
The continuous evolution of large language models necessitates novel approaches to address fundamental limitations like catastrophic forgetting, especially as models become more integrated into dynamic applications.
Improving continual learning for LLMs is critical for their long-term applicability and efficiency, reducing the need for costly retraining and enabling more adaptive AI systems.
This research proposes a new regularization method that shifts from weight-space to activation-space, potentially overcoming the 'polysemantic' issue in LLMs and enhancing their ability to learn continuously without forgetting past knowledge.
- · AI developers
- · Companies deploying AI agents
- · LLM researchers
- · Cloud service providers
- · Companies reliant on frequent, expensive LLM retraining
- · Traditional continual learning methods
More robust and adaptable large language models capable of integrating new information without significant knowledge decay.
Accelerated development and broader deployment of AI agents in dynamic environments, as models retain capabilities while acquiring new ones.
A potential reduction in the computational and energy demands associated with maintaining and updating frontier AI models, impacting the compute supply chain and energy bottleneck narratives.
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Read at arXiv cs.LG