Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education

Autor:	Manathunga, S. S., Illangasekara, Y. A.
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence H.3.1 J.3
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.00479 Zobrazit plný text záznamu View this record from Arxiv