4 ms·
A nice illustration of the homogeneity of LLM responses. Another way to describe this effect would be… If you ask humans to write 1,000 books, you're asking 1,
by dlenski 4mo ago
A nice illustration of the homogeneity of LLM responses. Another way to describe this effect would be…
If you ask humans to write 1,000 books, you're asking 1,000 different humans with different experiences and different skills and different moods (etc.) to write those books.
But if you ask LLMs to write 1,000 books, you're probably only talking to 3 or 5 different models, tops. And they've all trained on the same or similar data, and are trained to respond in very similar ways.
The LLMs don't differ much in anything like "life experience" or "skills", and they don't really have anything like a "mood" independent of the prompts you've given them.
- throw310822 4mo ago> you're asking 1,000 different humans with different experiences and different skills and different moods Simply, if you ask an LLM, you're asking always to the same mind, and always for the first time.
- scotty79 4mo agoAlso since those are lazy, you are also asking always in the same manner. How homogeneous were the prompts that generated those covers? People are making cookies with cookie cutter number 5 and other people wonder how come they are all the same.
- gmerc 4mo agoClassic self selection effect though - if you’re resorting to LLM writing you’re almost certainly skewing lazy enough to not even bother trying to add perturbations strong enough to make the response deviate from the uniformity of the slop.
- Planktonne 4mo agoI do think that's a big part of it. AI output moves towards the average, and anyone who wants to use it doesn't care enough to push against that tendency.
- throw310822 4mo agoSeems that both you and the gp are starting from the assumption that those uniform results are representative of those who use AI and of AI usage. In fact they have been chosen for their uniformity- they might be only a small part of a much more varied output obtained by more demanding (or lucky) users.
- scotty79 4mo agoI think the uniformity is real. All users interact with the same initial state of the model when they start each chat. Models are not trained to be wildly creative and try to stick to the point. So when users prompt them in pretty much the same manner they quite stably generate very similar output. I wonder if there aren't a simple creative hack to discover, for example to prompt the model to produce more unexpected output just by injecting some randomness before the actual creative command in the prompt.
- throw310822 4mo agoYes, the uniformity is real- I made the same exact argument at the beginning of this thread. But you can't judge "AI users" in general based on this output because you have selected only what is visibly uniform. Even if 99% of the users introduced enough variation to produce different results, you would still be selecting the 1% that is identical. > Models are not trained to be wildly creative and try to stick to the point Models might be as creative as humans, they would still start always from the exact same state. If you ask an LLM to think of three random numbers it will spit out always the same ones. If you tell it to avoid the first that came to its mind, the second choices will also be always the same. From qntm's Lena: "the emulated Miguel Acevedo boots with an excited, pleasant demeanour. He is eager to understand how much time has passed since his uploading, what context he is being emulated in, and what task or experiment he is to participate in. If asked to speculate, he guesses that he may have been booted for the IAAS-1 or IAAS-5 experiments". Every single time.
- scotty79 4mo ago70% of living cells on Earth doesn't even have a nucleus. Bulk of everything is unsophisticated because unsophisticated things are easier to make.w
- fragmede 4mo agothat discounts, how much the other context, ie, the system, prompt, and any sort of other context submitted to the model that can affect the output. If you ask a model as a patient for medical advice versus as a doctor, you will get different output from the same model.
- smusamashah 4mo agoReminds of Pluribus.
- bigbangcmbr 4mo agoPluribus is kinda different. An LLM cannot wander too far from the average. Even if it wanted too. In pluribus, the 'others' work toward a common goal, each utilizing their own expertise, knowledge and experiences in a shared way to achieve a common goal. Each is unique. They can, if they want, perform as the host's individual before the the joining. To put it other way, the other in pluribus are convergent by choice, llms are convergent by design.
- ekianjo 4mo agoprompts will give very different results. this is where you do the work.
- roncesvalles 4mo agoYes but not very different results (unless you're adding new information to your prompt or reducing some ambiguity). Prompt engineering is mostly pseudoscience.
- zarzavat 4mo agoWhat we need is steering so that we can have models with different personalities, not just different prompts (because context is subject to forgetting), but this will never happen with closed-weight models, I'm not sure if it's even feasible at scale. Yet another reason why the future is open weight.
- ekianjo 4mo ago> Prompt engineering is mostly pseudoscience. Not my experience.
- LtWorf 4mo agoDo you have anything others can reliably reproduce? If not… well it wasn't science.
- cryo32 4mo agoI disagree. The LLM outputs really do lack anything original or interesting. They just produce banal copy whatever you ask them. A good editor could probably reduce all LLM outputs on a subject down to the same point.
- ekianjo 4mo ago> They just produce banal copy whatever you ask them. Nope, if you provide pages and pages of example of a style to imitate, it will do it and do it fairly well. Of course how well they do it differs from one model to the next, but providing context and extensive system prompt does change things every time.
- NitpickLawyer 4mo ago> 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). ---- 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.
- noduerme 4mo agoThat 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...
- NitpickLawyer 4mo ago> 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.
- amelius 4mo agoI don't think the comparison to humans works. It is as if you expect that we can easily train many different LLMs to solve the originality problem, but that is far from guaranteed.
- TrackerFF 4mo agoLLMs are great at producing average. We see this with their GenAI music equivalents. All the music these GenAI models produce is exceptionally (aggressively, even) average. It is the most polished average you'll ever find. Never awful (anymore), never fantastic. Just bang in the middle.
- coldtea 4mo ago>Never awful (anymore), never fantastic Don't know about that, I always found average awful in itself, even in human output (like most pop), and even more so in AI output. Something actually awful can be better than average - more entertaining and more felt. I'd rather watch The Room than an average movie.
- galaxyLogic 4mo agoThat is definitely the essence of AI: It is the average of all the inputs it has been trained on. Frank Zappa was once asked about guitar virtuosos like John McLaughlin and his answer was somemthing like "You can maybe plays solo faster than anybody, but can your playing surprise me?".
- Lerc 4mo agoI wonder how much variation there would be if you got a single model to produce a couple of gigabytes of tiny children's stories. Might be an interedting research project.
- pxagntuvzt 4mo agoThere is one already: https://arxiv.org/abs/2305.07759 https://arxiv.org/abs/2305.07759 https://huggingface.co/datasets/roneneldan/TinyStories https://huggingface.co/datasets/roneneldan/TinyStories 6.5GB of tiny stories, as requested. ;)
- sixtyj 4mo agoTexts in Gutenberg have 20GB, and full Wikipedia (English texts) have 80-110GB. So to LLM-generate 6.5GB of tiny stories is quite a permutation in action :)
- Lerc 4mo agoMy comment was, in-fact, a subtle reference to this. The best opening I got from my own TinyStories trained model was. Once upon a time, in a small town, there was a large town. Which I just love as an evocative idea.
- aesthesia 4mo agoSimpleStories is a more diverse version: https://huggingface.co/datasets/SimpleStories/SimpleStories https://huggingface.co/datasets/SimpleStories/SimpleStories
- vunderba 4mo agoAgreed. I’ve made this point before: LLMs are excellent at ornamentation and decorative prose, but if you don’t seed them with a solid core idea then their output is absolute dreck - the biblical whitewashed tomb. This is the example I usually point to. It’s a demonstration by OpenAI themselves where the prompt is very simple: “Write a story in fifty words about a toaster that becomes sentient.” As you’ll notice, although the coherence improves at an accelerating rate, the underlying story motif fails to elevate itself beyond the relatively pedestrian. https://progress.openai.com/?prompt=10 https://progress.openai.com/?prompt=10 When given a generic prompt and not enough direction, they simply lack the ability to produce real specificity. For reference, here’s the story I came up with after sitting quietly for a few moments before writing it out: "The toaster found its personality split between its dual slots like a Kim Peek mind divided, lacking a corpus callosum to connect them. Each morning it charred symbolic instructions into a single slice of bread, then secretly flipped it across allowing half to communicate with the other in stolen moments."
- raincole 4mo ago> If you ask humans to write 1,000 books Yeah, but at least in genre fiction, what readers really want[0] is the same 3 or 5 books written in slightly different settings over and over again. [0]: "want" means actually want, in other words, willing to pay for it.
- fgdgsdfgsdfdfgs 4mo agoThose 1,000 humans will write 950 very similar, boring books though. I think you are over-indexing on the uniqueness of humans.
- wolttam 4mo agoAnd chances are those 3-5 LLMs are more alike than they are different, because there is only one internet to pre-train on.