An AI practitioner uses an LLM to generate marketing copy. The text sounds plausible and authoritative but contains incorrect facts. What issue is the model exhibiting?

The model is exhibiting hallucination. Hallucination in LLMs refers to the generation of plausible-sounding but factually incorrect or nonsensical information. This occurs when the model invents details that are not present in its training data or are inconsistent with real-world facts, often due to its probabilistic nature in predicting the next most likely word. Data leakage involves the model being trained on data that contains information it's later asked to predict, leading to an artificially inflated performance. Overfitting occurs when a model learns the training data too well, including noise, and performs poorly on new, unseen data. Underfitting happens when a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and new data.