Large language models (LLMs) have shown promise for generative and
knowledge-intensive tasks including question-answering (QA) tasks. However, the
practical deployment still faces challenges, notably the issue of
"hallucination", where models generate plausible-sounding but unfaithful or
nonsensical information. This issue becomes particularly critical in the
medical domain due to the uncommon professional concepts and potential social
risks involved. This paper analyses the phenomenon of hallucination in medical
generative QA systems using widely adopted LLMs and datasets. Our investigation
centers on the identification and comprehension of common problematic answers,
with a specific emphasis on hallucination. To tackle this challenge, we present
an interactive self-reflection methodology that incorporates knowledge
acquisition and answer generation. Through this feedback process, our approach
steadily enhances the factuality, consistency, and entailment of the generated
answers. Consequently, we harness the interactivity and multitasking ability of
LLMs and produce progressively more precise and accurate answers. Experimental
results on both automatic and human evaluation demonstrate the superiority of
our approach in hallucination reduction compared to baselines.

通过与大规模语言模型和数据集合作，本文分析了医学生成型问答系统中幻觉现象的问题，并提出了一种交互自我反思的方法来解决该挑战，最终实验证明该方法在幻觉减少方面优于基线模型。