> LLMs are ok at finding things that exist, but they have zero ability to abstract and find what is missing (actually, probably negative; they'd likely hallucinate and fill in the gaps).

I feel this is mostly a prompting issue. Specifically GPT-4 shows surprising ability to abstract to some degree and work with high-level concepts, but it seems that, quite often, you need to guide it towards the right "mode" of thinking.

It's like dealing with a 4 year old kid. They may be perfectly able to do something you ask them, but will keep doing something else, until you give them specific hints, several times, in different ways.

There are other ways of using the model other than iterative forward inference (completions). You could run the model over your novel (perhaps including a preface) and look at the posterior distribution as it scans. This may not be so meaningful at the level of the token distribution, but there may be interesting ways of “spell checking” at a semantic level level. Think a thesaurus but operating at the level of whole paragraphs.

Firstly, I don't at all agree that they have zero ability to abstract. Doesn't fit my experience at all. A lot of the tasks I use ChatGPT for is exactly to analyse gaps in specifications etc. And have it tell me what is missing, suggest additions or ask for clarifications. It does that just fine.

But I've started experimenting with the second part, of sorts, not to find plot holes but to have it create character sheets for my series of novels for my own reference.

Basically have it maintain a sheet and feed it chunks of one or more chapters and asking it to output an a new sheet augmented with the new details.

With a 100K context window I might just test doing it over while novels or much larger chunks of one.

that's not at all what I said