Comparative Performance Analysis of Large Language Models for Deployment in Women's Healthcare Mobile Applications

Large Language Models (LLMs) have sparked considerable interest among researchers across diverse disciplines considering their remarkable text generation and processing capabilities. Their capabilities have the potential to promise better health awareness among female users when integrated with women-specific chatbot services. Therefore, this study aims to compare the LLM models BERT, DistilBERT, Roberta, DialoGPT, TinyLLAMA, and GPT-2 by fine-tuning them on a health-specific dataset and evaluating their efficiency in generating information and engaging responses. Our analysis suggests that text-generation models are well-positioned to deliver more engaging answers and may prove instrumental in effectively enhancing health awareness among women.