Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models
arXiv:2606.00284v1 Announce Type: new Abstract: While continual pretraining~(CPT) is a practical way to extend large language models to new languages, na\"ive finetuning on targeted data erodes existing capabilities through catastrophic forgetting. Organizing training around language families reduces cross-language interference but cannot alone prevent forgetting of the general knowledge needed for downstream tasks. We link this forgetting to parameter drift in multilingual CPT and present a suite of five layer-aware parameter alignment strategies: hard layer freezing, soft regularization, pos
The paper addresses a core challenge in continually updating large language models for multilingual contexts, a critical area as AI expands globally and requires continuous adaptation.
This research provides a method to mitigate catastrophic forgetting, crucial for building more robust and adaptable AI models that can serve diverse linguistic populations without losing prior knowledge.
The ability to continually pretrain multilingual models without significant knowledge degradation improves the efficiency and effectiveness of extending AI capabilities to new languages and domains.
- · AI developers
- · Multilingual AI platforms
- · Users of diverse languages
- · Language technology sector
- · Companies relying on single-language AI
- · AI models without robust continual learning strategies
Improved performance and broader applicability of multilingual large language models.
Accelerated development and deployment of AI solutions in non-English speaking markets.
Enhanced global AI integration leading to more equitable access to advanced AI capabilities across language barriers.
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