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

1 Aug 2023arXiv:2308.00479archive 2025-07-28

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.

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ssm123ssm/docgpt-pharm officialmentioned in papermentioned on GitHub report

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Tasks

Abstractive Text Summarization Hallucination RAG Retrieval Retrieval-augmented Generation

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Methods

Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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