While PETL approaches offer significant advantages in terms of parameter efficiency and reduced computational overhead, they also come with certain drawbacks compared to full fine-tuning. One potential limitation is the risk of underfitting, particularly in complex tasks where the model may not have sufficient capacity to capture the intricacies of the data. This can lead to suboptimal performance, especially in scenarios where the downstream task requires a deep understanding of the data that a limited number of tunable parameters cannot provide.
Another limitation is the potential for reduced flexibility in adapting to new tasks. Full fine-tuning allows for the complete reconfiguration of the model's parameters, enabling it to learn task-specific features more effectively. In contrast, PETL methods may struggle to adapt to highly specialized tasks that require extensive modifications to the model's architecture or learned representations.
Additionally, PETL approaches may not fully exploit the representational power of large pre-trained models. By only tuning a small subset of parameters, PETL methods might miss out on the rich feature representations that could be learned through comprehensive fine-tuning. This could be particularly detrimental in tasks where nuanced feature extraction is critical for success.