Implementing an Enhanced Artificial Protozoa Optimizer-Based Fine Tuning Procedures for Large Language Models in Medical Question–answering System

Fine-tuning involves customizing a pre-trained Large Language Model (LLM) for a particular domain like medicine by retraining it on a smaller specialized dataset. This process helps the LLM to effectively manage complex data such as lengthy Electronic Health Records (EHRs) discharge summaries, task difficult for humans and traditional methods. This challenge was specifically significant in rapidly-evolving medical field. An LLM with outdated knowledge can generate inaccurate or fabricated information in high-stakes healthcare situations. This problem is compounded for non-English languages, which often lacks necessary benchmarking tools. In order to overcome these problems in the traditional method, a novel Fine Tuned LLMs (FTLLMs) method for a clinical question–answering model is implemented in this proposed system. At the earlier stage, the text essential for the evaluation is fetched from the benchmark resources. Further, these fetched texts are subjected to text pre-processing. In the pre-processing work, four different tasks are computed such as removal of punctuation and special characters, elimination of redundant and irrelevant data, removal of stop words, and stemming. Next, pre-processed text is fed for the prediction of the medical QA system using the proposed FTLLM. Moreover, parameters are fine-tuned by newly introduced Fitness Sorted-based Artificial Protozoa Optimizer (FSAPO). Through optimization process, performance metrics like [Formula: see text]1-score and the exact match were maximized. In addition, several functionality calculations are improved in the proposed Fine Tuned LLM (FTLLM) by optimizing the network for a medical QA system through existing methods. The newly designed FTLLM fine-optimizing model for the medical QA system is introduced in Python, and the detection process is also performed. Here, the superiority of the implemented model is contrasted with the previous systems and with the statistical metrics. A comparative analysis of Dataset 1 demonstrates that the proposed FSAPO-FTLLM algorithm, when utilizing the ReLU activation function achieves 91% increase in precision compared to existing performance baseline techniques. This ReLU activation function reduces 94.7% computational load by using threshold operation and provides faster performance training performance.