LLMs (large language models), can sometimes hallucinate or generate false information due to a few key reasons:

Overgeneralization – Models trained on very large datasets often learn to make broad generalizations. They may then apply these overly general rules in contexts where they do not apply, resulting in false or nonsensical outputs.

Overconfidence – Models can become extremely confident in their predictions, even when those predictions are wrong. This overconfidence can lead to hallucinated “facts” stated with high certainty.

Limited world knowledge – Despite being trained on large datasets, models have no real-world experience. This can result in responses that seem coherent but do not correlate with truth or reality.

Out-of-distribution inputs – When models receive inputs that are very different from their training data, they can start generating unrealistic or ungrounded outputs.

Optimized for likelihood – LLMs are optimized to simply predict the next most likely token. They are not trained to distinguish between true and false information.

No consistency checks – Humans constantly verify new information against their existing world knowledge. Models have no mechanisms to check consistency or coherence with known facts.

Overall, the tendency to hallucinate stems from models being trained to produce any response that best matches the statistical patterns in their training data, without any grounding in real world knowledge or common sense. Researchers are exploring techniques to make AI more truthful and verified.