Retrieval Augmented Generation and Representative Vector Summarization
  for large unstructured textual data in Medical Education
S. S. Manathunga Y. A. Illangasekara
Large Language Models are increasingly being used for various tasks including
content generation and as chatbots. Despite their impressive performances in
general tasks, LLMs need to be aligned when applying for domain specific tasks
to mitigate the problems of hallucination and producing harmful answers.
Retrieval Augmented Generation (RAG) allows to easily attach and manipulate a
non-parametric knowledgebases to LLMs. Applications of RAG in the field of
medical education are discussed in this paper. A combined extractive and
abstractive summarization method for large unstructured textual data using
representative vectors is proposed.