This research paper focuses on the challenges posed by hallucinations in
large language models (LLMs), particularly in the context of the medical
domain. Hallucination, wherein these models generate plausible yet unverified
or incorrect information, can have serious consequences in healthcare
applications. We propose a new benchmark and dataset, Med-HALT (Medical Domain
Hallucination Test), designed specifically to evaluate and reduce
hallucinations. Med-HALT provides a diverse multinational dataset derived from
medical examinations across various countries and includes multiple innovative
testing modalities. Med-HALT includes two categories of tests reasoning and
memory-based hallucination tests, designed to assess LLMs's problem-solving and
information retrieval abilities.
Our study evaluated leading LLMs, including Text Davinci, GPT-3.5, LlaMa-2,
MPT, and Falcon, revealing significant differences in their performance. The
paper provides detailed insights into the dataset, promoting transparency and
reproducibility. Through this work, we aim to contribute to the development of
safer and more reliable language models in healthcare. Our benchmark can be
found at medhalt.github.io

这篇研究论文关注于大型语言模型在医疗领域中所带来的幻觉挑战。通过提出一个新的基准和数据集（Med-HALT），该研究评估了几种先进的语言模型，并揭示了它们之间的显著差异。这项工作旨在为医疗保健中的语言模型的开发做出贡献，提高其安全性和可靠性。