This might be considerably different, and calling it "prior art" fails to consider what is actually going on here. The appearance may be similar, but lots of things can look similar while being completely distinct. And this is indeed such a case.

One of the words I got was "charlite" for the pale green colour of charcoal used as a dye. Charlite might not be a real word, but it is made up the same way a real word would be.

The method is important, because "charlite" probably came about by specifically asking GPT2 for a definition to the non-word "charlite."

In fact, this shows up in the source code examples:

# definition for a word you make up print(word_generator.generate_definition("glooberyblipboop"))

This is literally the opposite of what OP is presented, since we know where the "defined" word comes from with the GPT2 examples, which means that was a demo of GPT2 trying to work out a human provided word. It is literally a function of the program: generate_definition(). It was specifically written to do that.

But we don't know where the words come from, even though they are internally consistent, with the DALL-E 2 examples. As far as we can tell, it's an internal phenomenon not based on intentional human input.

Having said that, GPT2 probably has the same phenomenon. But the link you provided is not demonstrating that.

ok, so proposed study design, provide a sample of these along with obscure english words to a number of individuals, and get them to try pick out the real words.

From there take the selection of the fake words people ranked the most real.

Select a number of those words and get Dall-E 2 to try and make images of them, then see how many of those images contain results that represent the imaginary word.

If anyone who has access to Dall-E 2 wants to try this, I would _love_ to see the results.