For humans hallucinations are a particular class of error, so I find hallucination more descriptive than either error or bug.

I also think it’s relevant because a hallucinator often doesn’t recognize that the hallucination isn’t real. That’s more accurate for the LLM than either lie or confabulation, IMO. They algorithm is trained to produce strings of text that have semantic meaning based on some statistical likelihood of tokens appearing next to each other. The LLM algorithm is working as intended.

Hallucinations are also often emergent from a particular state or situation, which reflects the generative aspect of LLMs.

Hallucinations are sometimes resolved in humans by grounding exercises. “Touching grass.” The same is true for LLM hallucinations. Inaccuracies are found by cross-checking the output against an internet search or another LLM.