I wouldn't say it's easy to detect hallucinations. Understanding output token probability distributions is only part of a solution, and we still aren't perfect. Just better than individual models.

Hallucinations may seem rarer for a few reasons. First, models are more accurate with certain prompts. Second, models are more convincing when they do hallucinate. They may get an overall idea, but hallucinate the details. Hallucinations are still a major problem and are fundamental to the way modern LLMs work.