Are you hallucinated? Insights into large language models

The work presented in the paper challenges the common view of hallucination as a flaw in Large Language Models (LLMs), proposing instead that it is an inherent feature of their architecture. It explores the relationship between “creativity,” “generalization,” and “hallucination,” showing that they result from the transformer model’s high-dimensional vector blending. Using Gemini 2.0 and 2.5 models, the authors demonstrate a “Self-Correction Blind Spot,” where LLMs can recognize errors but lack the reasoning pathways to correct them. The paper introduces the “Generalization-Hallucination Trade-off,” suggesting that minimizing hallucination would impede generalization. The research advocates for managing hallucination with external verification rather than eliminating it.