From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem
Large language fashions (LLMs) fairly often generate “hallucinations”—assured but incorrect outputs that seem believable. Despite enhancements in coaching strategies and architectures, hallucinations persist. A brand new analysis from OpenAI offers a rigorous rationalization: hallucinations stem from statistical properties of supervised versus self-supervised studying, and their persistence is strengthened by misaligned analysis benchmarks. What Makes Hallucinations…
