> A nice illustration of the homogeneity of LLM responses. [...] And they've all trained on the same or similar data, and are trained to respond in very similar ways.

I mostly agree, but this is a very simplified explanation. The models are indeed trained to respond in similar ways, for "basic" prompts. And that's as much a feature as it is a bug. In other words, the bug becomes apparent only if you give 100+ basic prompts. But giving it 100+ basic prompts and expecting originality is a silly endeavour. That's not how you get originality.

The way I'd go about to generate 1000 books, while expecting different outcomes is something along these lines (and nowadays you can ask your favorite LLM to wire up this workflow for you, with decent outcomes):

1. Ask for a list of 20 features that define a book (genre, style, number of characters, tropes, plot, continuity, relationships, etc.)

2. For each feature, ask for a list of 50 examples, ordered from most common to the most unique.

3. Randomly pick 10 features, and for each pick one of the 50 generated items. Ask for the rest of the features to match the theme.

4. Ask for 10 possible book outlines that match the chosen features, randomly pick between 2-8.

5. Create a detailed prompt that includes all the above features, and ask for a synopsis for each chapter, given the above outline chosen.

6. Given {features} and {outline} and {synopsis} write chapter 1.

7. for each chapter in list, given {...} and (optional) previous matching chapter(s), write chapter n+1

(optional 8.) given {...} and 2-3 consecutive chapters, align the ending / beginning of a new chapter for style / features / continuity, etc.

(optional 9.) given {...} and the whole book, list chapters / paragraphs that don't match the given {...} and provide a list of 5 improvements. (randomly choose 1 and ask for an edit).

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Now, this probably won't give you something like cloud atlas, but they'll at least be different books. That's how I'd do it if I wanted to see how different they can write. Not 1000 "basic" prompts and expecting originality.

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noduerme 4 hours ago | parent [-]

That whole thing would get you 1000 variants of existing art. But if you asked a thousand different designers to do a cover for the same book...

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NitpickLawyer 3 hours ago | parent [-]

> 1000 variants of existing art.

This is very naive. I can almost guarantee that some combinations of 20 * 50 features will hit on something that has never been written before in that specific combination. And if that's still not enough, increase the number of features. Add more randomness, add more steering, add random steering in random chapters, change it up, and so on.

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noduerme 3 hours ago | parent | next [-]

I'm an art director. Finding a sequence that hasn't been hit in that specific combination is not sufficient to justify paying someone $150 an hour to go be creative.

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spwa4 3 hours ago | parent | prev [-]

> Add more randomness, add more steering, add random steering in random chapters, change it up, and so on.

That doesn't work for AI models. The whole training process depends on the basic principle that if you take the average of 100, in this case book cover designs, that the average is less like randomness than any individual cover you've used to make your average.

So the output will, by necessity, be closer to the average.

The human learning algorithm is much, much more data efficient than models. A absolute top human expert will have read/seen/heard/talked/... about 160 million "tokens" (that's about 2000 books). Frankly, the nerve inputs of all experiences of an entire human life, from baby to rewriting relativity theory, are only a couple dozen gigabytes.

Qwen 3.6 27B has been trained (as in seen ~10 to ~50 times) 8 trillion tokens, or to put it another way: for every second you will have spent "gathering life experiences" (ie. your whole life) on your deathbed Qwen 3.6 27B has spend about 50.000 seconds learning. And really that figure should be multiplied by the 10 or 50 training iterations.

Add another 3 or so orders of magnitude and you've got ChatGPT. By this measure, the human brains outperforms ridiculously overspecced ML models (because that's what ChatGPT and the like are) in efficiency a factor of by 5 million or more. This is the reason humans are still faster than ML models.

As for human training iterations: we can be simple: it's 1. In fact, it's impossible to make it even 2. Of course, when it comes to human performance: we are a better but not fundamentally different version of genetic algorithms. Do most humans perform? The honest answer is no. 1 in 1000, and that's very generous, improves SOTA. You absolutely need the 1000 failures though, as anyone whose tried a PhD (or even just design a large program) knows.

So we are very far away from allowing AI models to do what humans can do: take one example and produce, from one example, a better output. And there will always be much more variation in that approach. But ... most human attempts to do something are total crap. Most AI attempts to do something will succeed, but they'll be comparatively be bland, tasteless, "without soul", ...

And this is ignoring the problem that AI also has a massive limitation (that can't be solved, no matter how many nvidia cards you have) in that it trains against historical data. And counterfactuals don't work. What would have happened had Shakespeare decided Macbeth's wife was a force for good? Would the king still get murdered? Would it still be a great story? You can't work with counterfactuals.

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NitpickLawyer 2 hours ago | parent [-]

> That doesn't work for AI models.

Of course it does. I know it does because I've been using variations of this workflow since gpt3.0. In fact it's the only way it can work, since by design LLMs work from left to right. You can't expect it to produce original stuff if you don't give it the anchors for what original means. It'd be like going to a new bar every night and asking for a "beer that you haven't had before". There's no information to work on there.

	▲	spwa4 an hour ago \| parent [-]
		The point was to take a random combination of story elements. Pick one each {King,dad,CEO} {betrays,kills,loves} {his enemy,the king,a foreign prime minister} and feed to an LLM. The output will not be an intricate well designed epic storyline, but a cookie-cutter boring snoozefest. BUT you can give that to a bunch of humans, who "insert their life experience" (ie. parts of their training data, translated to LLM terms) and sometimes out comes Game of Thrones, Star Wars, ...