[2605.30400] Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow
[Submitted on 28 May 2026]
Title:Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow
View a PDF of the paper titled Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow, by Ahmed Abdeen Hamed and 1 other authors
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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.
Comments: Main Manuscript and Supplementary Information. Both are equally important
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2.7; I.7.3
Cite as: arXiv:2605.30400 [cs.CL]
(or arXiv:2605.30400v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.30400
arXiv-issued DOI via DataCite (pending registration)
Journal reference: STAR Protocols, 2026; 7
Related DOI:
https://doi.org/10.1016/j.xpro.2026.104533
DOI(s) linking to related resources
Submission history
From: Ahmed Abdeen Hamed Ph.D [view email] [v1] Thu, 28 May 2026 16:01:24 UTC (2,289 KB)
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