Optimized Medical Recommendation System Utilizing Large Language Models for Enhanced Question Answering Performance

In the era of fast expanding online medical filed, getting recommendations using AI technology is a challenging one. This study presents a methodology for fine-tuning GPT-3.5 Turbo-0125 model on the MedQuAD dataset, aiming to enhance performance in recommendations of medical question-answering systems. The dataset is prepared by concatenating question-answer pairs and truncating text sequences to standardized lengths. Using Byte Pair Encoding (BPE) for tokenization and optimizing hyperparameters, the model is fine-tuned to improve accuracy and contextual relevance. The evaluation, employing BLEU and ROUGE metrics, indicates substantial performance enhancements. This research underscores the potential of fine-tuning advanced language models for improved medical recommendations and personalized healthcare solutions.