Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education
This addresses domain-specific alignment for LLMs in medical education, though it appears incremental as it builds on existing RAG and summarization techniques.
The paper tackles the problem of hallucination and harmful outputs in large language models (LLMs) for medical education by proposing a combined extractive and abstractive summarization method using representative vectors, integrated with Retrieval Augmented Generation (RAG) to attach non-parametric knowledge bases.
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.