Random Initialization of Gated Sparse Adapters

A novel technique called Random Initialization of Gated Sparse Adapters (RIGSA) has been introduced to address the problem of catastrophic forgetting in language models during fine-tuning. This issue, common in the domain of language model adaptation, involves the loss of previously learned knowledge when models are trained on new tasks. Unlike traditional methods such as LoRA, RIGSA employs sparse adaptation without imposing rank constraints, which distinguishes it from existing approaches. The key feature of RIGSA is its ability to maintain model performance across new tasks by mitigating forgetting effects. Comparative analysis indicates that RIGSA offers a promising alternative to LoRA, potentially enhancing the effectiveness of fine-tuning processes. This advancement may contribute to more robust and adaptable language models in various applications. The development reflects ongoing efforts to improve model generalization while preserving prior knowledge.