AI Hallucinations Quiz

Answers and explanations appear as you take the quiz.

1. Why does next-token prediction alone fail to verify a factual claim in a generated answer?
   • Every model is connected to only unreliable websites
   • All users ask grammatically incorrect questions
   • A plausible continuation is not the same as evidence that the statement is true
   • A fluent answer automatically proves the fact
2. Why are hallucinations particularly dangerous?
   • They are always written in broken grammar so users ignore them
   • They only appear in error messages
   • They cause the model to crash
   • They are delivered fluently and confidently, so errors are easy to miss
3. Does retrieval-augmented generation (RAG) completely eliminate hallucinations?
   • Yes, RAG guarantees factual answers
   • No, models can still contradict or ignore noisy or conflicting retrieved text
   • Yes, but only on weekends
   • No, because RAG never provides any documents
4. Which is a genuine example of a hallucination?
   • The model citing a court case that does not exist
   • The model refusing to answer a question
   • The model asking a clarifying question
   • The model returning a blank response
5. Which workflow can help check a generated factual answer?
   • Trust every citation because it looks formal
   • Treat a low sampling temperature as proof of correctness
   • Hide the evidence so the answer is easier to read
   • Retrieve relevant material and verify whether its statements and citations support the claims
6. How should a claim that a new model has eliminated hallucinations be assessed?
   • Accept it solely because the announcement sounds confident
   • Assume one successful example proves every application
   • Use defined tasks, evidence, and measurement conditions rather than assume universal reliability
   • Judge only whether the model produces polished prose
7. A generated answer cites a real page, but the page does not support the associated factual statement. What remains unverified?
   • The support for that particular claim
   • Whether a URL can be opened
   • Whether the model used fluent sentences
   • Whether the citation contains text
8. A supplied document says delivery takes five business days, but its generated summary says two. Which check reveals the error?
   • Counting how many sentences the summary contains
   • Comparing the summary's quantity with the source document
   • Checking only whether the prose sounds confident
   • Assuming summaries may replace any source number