Yup: prompt a large language model to tell you about itself and it will sometimes weave weird tales. For example, I just had the following interaction with GTP2large[0]:
Me: What is your name?
Model: I'm Mark A. <continue> Dreyer, and I'm with the <continue> Federal Communications Commission (FCC)
Obviously, it's not actually a guy named Mark A. Dreyer and, as far as I can tell, there is no Mark. A. Dreyer in a prominent role at the FCC. That, as far as I'm concerned, is essentially a hallucination.
If you think about how they work, this isn't totally surprising. The model has been programmed to take the input, turn it into a series of tokens, and then produce another series of tokens that are especially likely to follow the input ones. That's it.
To the extent that this corresponds to reality, it's only because reality is overrepresented in the training data, and so token sequences corresponding to reality are often more likely under its model. This also explains how it goes off the rails: Mark is a very common name (in the top 25 for men for decades) and once you generate a stereotypical middle-aged white guy name, it's not a huge leap to him being a bureaucrat, etc. I forgot to delete the huggingface boilerplate, which talks about advances in technology (etc), which probably sent it down the FCC route too.