A Novel Framework by Integrating LoRA and LIME for Efficient Fine-tuning of LLaMa 2 Model for Healthcare Multiple Choice Question Answering Tasks

The new model presented in the paper combines Low-Rank Adaptation (LoRA) and Local Interpretable Model-agnostic Explanations (LIME) on the efficacy of robustly fine-tuning the LLaMa 2 model to new tasks in the context of healthcare multiple-choice question-answering. The proposed method takes advantage of LoRA to save on computational resource consumption and uses LIME to increase interpretability to create transparency in clinical decision-making. Compared to the standard fine-tuning processes, experimental performance is better in terms of both efficiency and explainability. This architecture will provide a bright direction to implement large language models in the health sector with flexibility, stability, and explanation of the model to the medical profession.