why large language model (LLMS) hallucinate?
Large Language Models (LLMs) "hallucinate" when they generate information that is plausible-sounding but factually incorrect, nonsensical, or unfaithful to the input prompt. It's a key challenge in making LLMs reliable. Here's a breakdown of why this happens: 1. The Nature of LLMs: Pattern Recognizers, Not Knowledge Engines * Statistical Prediction: Fundamentally, LLMs are trained to predict the next most probable word (or "token") in a sequence based on the vast amount of text data they've processed. They are excellent at identifying statistical patterns, grammar, and style. * Lack of True Understanding/Reasoning: Unlike humans, LLMs don't "understand" concepts in the way we do, nor do they possess real-world common sense or consciousness. They don't have personal experiences or an internal model of reality. Their "knowledge" is embedded in the statistical relationships between words and phrases. * Optimized for Fluency, N...