arXiv Open Access 2023

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

S. S. Manathunga Y. A. Illangasekara

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Abstrak

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.

Topik & Kata Kunci

cs.CL cs.AI

Penulis (2)

S. S. Manathunga

Y. A. Illangasekara

Format Sitasi

APA MLA BibTeX

Manathunga, S.S., Illangasekara, Y.A. (2023). Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education. https://arxiv.org/abs/2308.00479

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓