Understanding Large Language Model Hallucinations: Exploring Causes, Detection Methods, Prevention Strategies, and Ethical Implications for Reliable AI Applications

This presentation delves into Large Language Model (LLM) hallucinations—incorrect or fabricated outputs that undermine reliability. It covers their causes (e.g., data limitations, transformer architecture), detection methods (like semantic entropy), prevention strategies (fine-tuning, RAG), and ethical concerns (misinformation, bias). The role of tokens and MLOps in managing hallucinations is explored, alongside the feasibility of hallucination-free LLMs. Designed for researchers, developers, and AI enthusiasts, it offers insights and practical approaches to enhance LLM accuracy and trustworthiness in critical applications like healthcare and legal systems.