The authors propose a new approach called Layer-Selective LoRA (LS-LoRA) for fine-tuning large language models (LLMs) on specialized tasks while retaining general capabilities. They introduce a lightweight proxy for ranking layer sensitivity using input-output cosine similarity, which helps identify layers that should be adapted for better performance and capability retention.