ReMAG-KR: Retrieval and Medically Assisted Generation with Knowledge Reduction for Medical Question Answering
Sidhaarth Murali, Sowmya S., R Supreetha · 2024
Large Language Models (LLMs) have significant potential for facilitating intelligent end-user applications in healthcare.However, hallucinations remain an inherent problem with LLMs, making it crucial to address this issue with extensive medical knowledge and data.In this work, we propose a Retrieve-and-Medically-Augmented-Generation with Knowledge Reduction (ReMAG-KR) pipeline, employing a carefully curated knowledge base using cross-encoder re-ranking strategies.The pipeline is tested on medical MCQ-based QA datasets as well as general QA datasets.It was observed that when the knowledge base is reduced, the model's performance decreases by 2-8%, while the inference time improves by 47%.