Definition
A parameter-efficient fine-tuning method that freezes base-model weights and trains smaller low-rank update matrices.
Distinguish it from nearby terms
Full fine-tuning may update all model weights. LoRA learns a compact set of changes that can be stored and deployed separately from the base model.
Check your understanding
LoRA reduces the number of trainable parameters, but its quality and safety still depend on the base model, data, objective, and evaluation.