LoRA was the parameter-efficient fine-tuning method adopted in this work. Here, the perturbation to weight matrices of the base model during fine-tuning is approximated using matrices of lower rank (called update matrices) using low-rank decomposition. During LoRA fine-tuning, only the update matrices are altered keeping the pre-trained weight matrices fixed, and subsequently the matrices are merged. In the LoRA training of all four pre-trained models, the query and value projection matrices of all attention layers were adapted using LoRA, the same as in the default setting of the original implementation. Among other weights, only the classification head weights are updated during the training.