Accuracy and hallucination are two ends of a spectrum.

If you turn hallucinations to a minimum, the LLM will faithfully reproduce what's in the training set, but the result will not fit the query very well.

The other option is to turn the so-called temperature up, which will result in replies fitting better to the query but also the hallucinations go up.

In the end it's a balance between getting responses that are closer to the dataset (factual) or closer to the query (creative).