AI Research
MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their limited parameter space causes catastrophic forgetting. While memory-based methods naturally address t...
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ArXiv CS.AI
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ArXiv CS.AI
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This research tackles the critical challenge of continual learning in smaller language models, which are vital for resource-constrained AI. MIITA's novel approach allows these models to adapt to new information without forgetting previous knowledge, overcoming limitations of larger models.
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