  Despite impressive advances in Natural Language Generation (NLG) and Large
Language Models (LLMs), researchers are still unclear about important aspects
of NLG evaluation. To substantiate this claim, I examine current
classifications of hallucination and omission in Data-text NLG, and I propose a
logic-based synthesis of these classfications. I conclude by highlighting some
remaining limitations of all current thinking about hallucination and by
discussing implications for LLMs.
