Parameter-efficient fine-tuning (PEFT) is most effective when your downstream task is similar to the pretraining data—such as classification or regression on related datasets—because the pretrained model already contains much of the required knowledge. When the domain shift is small and the task uses learned representations, PEFT can adapt the model with minimal parameter updates.

PEFT is limited when you need to update embeddings (for new vocabulary or very different inputs), or if the downstream task requires a different model architecture. Large distributional drift or major changes in data can also cause PEFT to fail, as its update capacity is restricted. Low-rank adapters or bottlenecked update mechanisms may underfit on complex tasks due to limited expressiveness.

Compared to full fine-tuning, which updates all parameters for maximum flexibility, and zero-shot use, which makes no adaptation, PEFT offers a balance: some adaptation with fewer trainable parameters. This improves efficiency but reduces expressive power. Always evaluate your task and data to decide if PEFT is appropriate.