Type II Hallucination

Type II hallucinations in large language models (LLMs) refer to the generation of factually incorrect or fabricated information in response to specific, often object-based, queries, contrasting with more open-ended "Type I" hallucinations. Current research focuses on developing methods to detect and mitigate these hallucinations, employing techniques like retrieval-augmented generation (RAG), fine-tuning, prompt engineering, and multi-agent debate approaches to improve model accuracy and reliability. Understanding and addressing Type II hallucinations is crucial for building trustworthy LLMs, particularly in high-stakes applications like medicine, where factual accuracy is paramount.