Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow
This protocol addresses the problem of evaluating the reliability and accuracy of LLM-generated biomedical associations for researchers and practitioners in the biomedical domain.
This paper presents a protocol for evaluating ChatGPT's ability to generate and verify disease-centric biomedical associations. It outlines methods for generating associations, validating biological entities using ontologies, and verifying associations through literature, including a RAG-enabled, cross-model majority voting workflow to semantically verify content and expose hallucinations.
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.