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Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow

2026-05-28 16:01:24
Ahmed Abdeen Hamed, Luis M. Rocha
arXiv_CL
               
Abstract

We present a protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. It outlines how we generate the associations, validate the biological entities using biomedical ontologies, and verify associations using literature. The protocol includes a self-consistency strategy to assess generative reliability across ChatGPT models. To address ontology exact-match limitations, we provide a use case performing semantic verification through a workflow enabled by Retrieval-Augmented Generation (RAG) powered by open-source large language models (LLMs). This enables LLMs to establish truth over content generated by other LLMs and expose hallucination.

Abstract (translated)

URL

https://arxiv.org/abs/2605.30400

PDF

https://arxiv.org/pdf/2605.30400.pdf

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