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Reference Accuracy in Large Language Model Chatbots: A Metric for Inherent Misinformation?

Małgorzata Pastucha1,2, Henryk Skarżyński2,3, Krzysztof Kochanek1,2

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Testing large language model (LLM) chatbots on reference accuracy reveals significant misinformation. ChatGPT-4.1 with web search performed best, highlighting the need for AI tool refinement in academic and clinical settings.

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Bibliometrics

Background:

  • Large language models (LLMs) and AI tools are increasingly used in academic research and clinical decision-making.
  • Assessing the accuracy of references provided by LLMs is crucial for identifying misinformation.
  • Verifying bibliographic data offers a quantifiable method for rating AI misinformation levels.

Purpose of the Study:

  • To compare the reference accuracy of different versions of ChatGPT and Gemini chatbots.
  • To evaluate the reliability of AI-generated references for academic and clinical use.
  • To establish a benchmark for rating AI misinformation in bibliographic retrieval.

Main Methods:

  • Six chatbot versions (3 ChatGPT, 3 Gemini) were tested.
  • Chatbots provided references for 25 highly cited otorhinolaryngology topics.
  • A total of 1947 references were verified against PubMed, Web of Science, and Google Scholar for accuracy.

Main Results:

  • Common errors included incorrect author names and DOI numbers.
  • ChatGPT-4.1 with web search achieved the highest accuracy (51%), followed by Gemini 2.5 Pro (41%).
  • Chatbots with web search capabilities outperformed those without; higher cited topics had lower error rates.

Conclusions:

  • AI-driven bibliographic retrieval requires significant refinement for reliable academic and clinical integration.
  • Reference accuracy testing provides a valuable metric for assessing LLM misinformation.
  • Current AI tools necessitate careful validation before widespread adoption in scientific and medical fields.