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AIF-C01 Practice Question: An AI practitioner is evaluating a text…
An AI practitioner is evaluating a text generation model and notices that the model sometimes produces plausible-sounding but factually incorrect statements. What is this phenomenon called?
Answer choices
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Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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Hallucination
Hallucination in LLMs refers to generating content that is not grounded in the training data or provided context. It is a known challenge for generative models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Hallucination
Why this is correct
Hallucination describes a model generating fluent, plausible output that is factually wrong or unsupported by its training data. It directly matches the stem's constraint: text that sounds credible yet is incorrect. This arises because generative models predict likely token sequences rather than verifying truth against a source.
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Catastrophic forgetting
Why it's wrong here
Catastrophic forgetting is the loss of previously learned knowledge when a model is retrained on new data, so it cannot explain fluent false statements in a static model. It is tempting because both involve degraded factual output, but forgetting requires sequential training, which the scenario does not describe.
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Bias amplification
Why it's wrong here
Bias amplification is the exaggeration of existing societal biases in training data, producing skewed or stereotyped outputs rather than arbitrary factual invention. It is tempting because both are output-quality harms, but the stem describes fabricated facts, not skewed representation, so bias amplification does not apply.
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Overfitting
Why it's wrong here
Overfitting is a training pathology where a model memorises training data and generalises poorly to unseen inputs; it does not describe fluent fabrication. It is tempting because overfitted models do emit confidently wrong outputs, but the stem's plausible-sounding false statements are hallucinations, which occur in well-fitted models too.
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