Article

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context

2024-10-18

Abstract excerpt

We created GNQA, a generative pre-trained transformer (GPT) knowledge base driven by a performant retrieval augmented generation (RAG) with a focus on aging, dementia, Alzheimer’s and diabetes. We uploaded a corpus of three thousand peer reviewed publications on these topics into the RAG. To address concerns about inaccurate responses and GPT ‘hallucinations’, we implemented a context provenance tracking mechanism...

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Literature Corpus work: 18fd7605-99a9-5a41-831e-40fe35393114
DOI: 10.1101/2024.10.16.618663

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