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

<ns3:p>Background We created the GeneNetwork Question Answer system (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. Methods 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 that enables researchers to validate responses against the original material and to get references to the original papers. To assess the effectiveness of contextual information we collected evaluations and feedback from both domain expert users and ‘citizen scientists’ on the relevance of GPT responses. Results When evaluating the responses to their questions, human respondents give a “thumbs-up” 76% of the time. Meanwhile, RAGAS scores 90% on answer relevance on questions posed by experts. And when GPT generates questions, RAGAS scores 74% on answer relevance. Discussion A key innovation of our study is automated evaluation by way of a RAG assessment system (RAGAS). RAGAS combines human expert assessment with AI-driven evaluation to measure the effectiveness of RAG systems. With RAGAS, we created a benchmark that can be used to continuously assess our knowledge base's performance. Full GNQA functionality is embedded in the free GeneNetwork.org web service, an open-source system containing over 25 years of experimental data on model organisms and humans. The code developed for this study is published under a free and open-source software license at <ns3:ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri" ns4:href="https://git.genenetwork.org/gn-ai/tree/README.md">https://git.genenetwork.org/gn-ai/tree/README.md</ns3:ext-link>.</ns3:p>