The rapid advancement of large language models (LLMs) has significantly impacted various domains, including healthcare and biomedicine. However, the phenomenon of hallucination, where LLMs generate outputs that deviate from factual accuracy or context, poses a critical challenge, especially in high-stakes domains. This paper conducts a scoping study of existing techniques for mitigating hallucinations in knowledge-based task in general and especially for medical domains. Key methods covered in the paper include Retrieval-Augmented Generation (RAG)-based techniques, iterative feedback loops, supervised fine-tuning, and prompt engineering. These techniques, while promising in general contexts, require further adaptation and optimization for the medical domain due to its unique demands for up-to-date, specialized knowledge and strict adherence to medical guidelines. Addressing these challenges is crucial for developing trustworthy AI systems that enhance clinical decision-making and patient safety as well as accuracy of biomedical scientific research.

本研究针对大型语言模型在医疗领域中幻觉现象的问题进行探讨，特别是其输出偏离事实准确性或上下文的情况。文章提出了一系列技术，如基于检索增强生成的技术、迭代反馈循环和监管微调，强调这些方法需要在医疗领域进行进一步的适应与优化，以提高临床决策和患者安全的准确性。

朝着可靠的医学问答：缓解语言模型幻觉的技术与挑战