Language models sometimes state false information fluently, a problem often called hallucination. A major focus has been reducing this by grounding answers in retrieved sources, training models to express uncertainty, and having them check their own work before replying.
Progress means systems that more often say they do not know, or cite where an answer came from, rather than inventing details. None of this eliminates the problem entirely, so important uses still pair the model with verification and human review. The goal is a system whose confidence better matches how reliable its answer actually is.