While the use of PEFT subspaces for Bayesian inference presents several advantages, there are notable limitations and drawbacks that need to be considered:
Approximation Errors: The low-rank approximations used in PEFT methods may lead to loss of information, potentially resulting in suboptimal performance. This can be addressed by carefully selecting the rank parameters and conducting thorough hyperparameter tuning to find a balance between efficiency and accuracy.
Limited Expressiveness: PEFT subspaces may not capture the full complexity of the original model, especially in high-dimensional parameter spaces. To mitigate this, researchers can explore hybrid approaches that combine PEFT with other techniques, such as ensemble methods or more complex Bayesian approximations, to enhance expressiveness.
Computational Overhead: Although PEFT methods reduce the number of parameters to be fine-tuned, they still require additional computations for Bayesian inference, such as sampling from posterior distributions. This can be addressed by optimizing the sampling process, using techniques like variational inference or more efficient sampling algorithms.
Dependence on Initial Conditions: The performance of Bayesian inference can be sensitive to the choice of initial conditions, such as the starting point of the fine-tuning process. To address this, multiple initializations can be tested, and ensemble methods can be employed to average out the effects of poor initial conditions.
Scalability: As the size of the model increases, the complexity of managing PEFT subspaces and performing Bayesian inference can become challenging. This can be tackled by developing more scalable algorithms and leveraging distributed computing resources to handle larger models efficiently.
By recognizing these limitations and implementing strategies to address them, the effectiveness of PEFT subspaces for Bayesian inference can be significantly enhanced.