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

arXiv 2605.30400 ↗First posted 28 May 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 28 May 2026.

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References · 7

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  2. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  3. 2024The Faiss librarycited by 376
  4. 2021Shifting Polarization and Twitter News Influencers between two U.S. Presidential Electionscited by 8
  5. 2025From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPTcited by 4
  6. 2023Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verificationcited by 3
  7. 2024Accelerating Complex Disease Treatment through Network Medicine and GenAI: A Case Study on Drug Repurposing for Breast Cancercited by 3

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  1. 2026MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation
  2. 2025OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models
  3. 2023Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
  4. 2025Leveraging Language Models and RAG for Efficient Knowledge Discovery in Clinical Environments
  5. 2024Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models
  6. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  7. 2025BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generation
  8. 2026Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field