Article

Creating a Biomedical Knowledge Base by Addressing GPT's Inaccurate Responses and Benchmarking Context

2024-11-27

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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Genomics and Rare Diseases

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Literature Corpus work: faf936b4-db06-5dd2-b701-aa647dc29b51
DOI: 10.32388/1amker

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