Yes of course it can, because they fit in the context window. But this is an awful test of the model's capabilities because it was certainly trained on these books and websites talking about the books and the HP universe.
I know, for instance, that gpt4 does much better with the python manual when we quote relevant context, even though it was trained on the python manual. This suggests pretraining is less than perfect.
Likewise, in the Harry Potter case I expect a significant difference between its background knowledge and the context enhanced trial. But I don't have intuition about the effect size we should expect! That makes it a fun experiment.
The linked X post shows that the user asked the model to generate a graph of the characters, which was presumably a novel question. This is a legitimate test of the model's ability to understand and answer questions about the training data. Repeating the books in the prompt for emphasis makes sense, since the model probably didn't memorize all the relevant details.
I wouldn't call it disparate when there's about a dozen wikis each spelling it out like this: https://harrypotter.fandom.com/wiki/Severus_Snape
Probably 80% of my questions to ChatGPT were about Harry Potter plot and character details as my kid was reading the books. It was extremely knowledgeable about all the minutiae, probably thanks to all the online discussion more than the books themselves. It was actually the first LLM killer app for me.
I have the same problem with benchmarks that use real world tests (like SAT/LSAT/GRE or whatever else). The model got a good score, sure, but how many thousands of variations of this exact test was it trained on? How many questions did it encounter that were similar or the same?
There is zero chance this is anywhere in the models dataset, and we were able to perform basic translation to and from English.