Large language models (LLMs) very often generate “hallucinations”—confident yet incorrect outputs that appear plausible. Despite improvements in training methods and architectures, hallucinations persist. A new research from OpenAI provides a rigorous explanation: hallucinations stem from statistical properties of supervised versus self-supervised learning, and their persistence is reinforced by misaligned evaluation benchmarks. What Makes Hallucinations Statistically […] The post From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem appeared first on MarkTechPost.
From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem
Large language models (LLMs) very often generate “hallucinations”—confident yet incorrect outputs that appear plausible. Despite improvements in training methods and architectures, hallucinations persist. A new research from OpenAI provides a rigorous explanation: hallucinations stem from statistical properties of supervised versus self-supervised learning, and their persistence is reinforced by misaligned evaluation benchmarks. What Makes Hallucinations Statistically […] The post From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem appeared first on MarkTechPost.