121–130 of 140 posts
interesting that GPT Image-2 managed to 2-shot this with thinking turned on, I didn't save a copy and it disappeared from my window but I first got a failure very similar to the one in the article, but it saw the issue and said it was going to use a reference image, after which it came out with https://i.imgur.com/hlWpQNT.jpeg
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It's not novel in the sense that nobody knew about img2img. It's novel in the sense that nobody thought of using img2img to solve this problem in this way.
It's novel if you never played with img2img, including especially several forms of (text+img)2img. Or, if you never tried editing images by text prompt in recent multimodal LLMs. That said, I spent plenty of time doing both, and yet it would probably take me a while to arrive at this approach. For some reason, the "draw a sketch, have a model flesh it out" approach got bucketed with Stable Diffusion in my mind, and m…
Another thing I’ve gotten very used to doing is avoiding the “one-shot” approach. If I generate something and don’t like the results, I bring it into Krita, move things around, redraw some elements, and then send it back in with instructions to just clean it up (remove any smudges or imperfections). The state-of-the-art models can do an astonishing job with that workflow.
> Transform this image into a photographed claymation diorama of assorted artisan chocolates and candies […] viewed from a low-angle Side note: whenever I read prompts for image generation, I notice very specific details which the model obviously ignored. Here the chocolates / candies in the last two images look anything but artisanal. They look very "sterile" and mass-produced. The viewing angle is also not accurate…
I believe most detailed prompts are AI generated.
Original Prompt: "Man with Trapezoid Head"
AI Expansion:
Portrait of a man with a trapezoid-shaped head, sharp geometric facial structure, angular jawline wider at the top and narrowing toward the chin, realistic skin texture, detailed pores, dramatic studio lighting, ultra-detailed, 85mm lens, shallow depth of field, dark neutral background, cinematic, photorealistic, 8k resolution.
I wonder whether this could be used to fine-tune image models to provide better outputs. Something like this: 1. Algorithmically generate a underdrawing (e.g. place numbers and shapes randomly in the underdrawing) 2. Algorithmically generate a description of the underdrawing (e.g. for each shape, output text like "there is a square with the number three in the top left corner). You might fuzz this by having an LLM re…
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That paper’s abstract doesn’t carry its title, to put it mildly.
What part of "Specifically, we define a formal world where hallucination is defined as inconsistencies between a computable LLM and a computable ground truth function. By employing results from learning theory, we show that LLMs cannot learn all the computable functions and will therefore inevitably hallucinate if used as general problem solvers. " doesn't carry the title, to ask mildly?
What they really prove is that it's impossible to extrapolate unconstrained non-continuous function from a finite subset of its values. Good for them, I guess.
It's like saying that the no free lunch theorems proves that LLMs can't be the best optimizers, while it proves (roughly) that the best optimizers don't exists. That is, even people aren't the best optimizers, but we manage somehow, so LLMs can too.
> Transform this image into a photographed claymation diorama of assorted artisan chocolates and candies […] viewed from a low-angle Side note: whenever I read prompts for image generation, I notice very specific details which the model obviously ignored. Here the chocolates / candies in the last two images look anything but artisanal. They look very "sterile" and mass-produced. The viewing angle is also not accurate…
I wonder how long it took to come up with all this? Because if I wanted a spiral of little "buttons" like the last one at the end (and they don't look very much like sweets) I'd be able to knock that out in Blender in an afternoon, and I'm not very good at Blender.
1. Prompt to make SVG - review in browser, iterate.
2. Prompt to write image prompt - review in editor, refine
3. Send to Gemini, get image
So maybe 5-10 mins.
I don’t know how to use Blender.
Also this method can be done over WhatsApp/telegram which is another plus over Blender type approach.
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I don’t agree with that definition of “hallucination”, for starters.
So substitute another phrase, if you prefer. It doesn't change the logic. "Specifically, we define a formal world where bungling is defined as inconsistencies between a computable LLM and a computable ground truth function. By employing results from learning theory, we show that LLMs cannot learn all the computable functions and will therefore inevitably bungle if used as general problem solvers."
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> character counting The models now whaste a vast amount of useless neurons memorising the character count the entire English language so that people can ask how many r's are in strawberry and check a tickbox in a benchmark. The architecture cannot efficiently or consistently represent counting letters in words. We should never have forced trained them to do it. This goes for other more important "skills" that are un…
> The models now whaste a vast amount of useless neurons memorising the character count the entire English language No they don’t. They only need to know the character count for each token, and with typical vocabularies having around 250k entries, that’s an insignificant number for all but the tiniest LLMs.
Those "tolkens" humans "count" are translated to a ~2048 (depends on model) floating point vector.
bird => {mamal, english, noun, Vertebrate, aviant} has one r but what if you make it 20% more "french". Is is still 1 r? That could be the word "bird" in french, or it could be a french speaking bird or a bird species common in france.
If nearest neibour distance to the vocabulary of every language makes the vector no longer map to "bird"; then the amount of rs' must change, using a series of trained conditional checks (with some efficiency where languages have some general spelling patterns).
That is such an unreasonable amount of compute, that it is likley faar cheaper, easier and more reliable to train the model to memorise the output:
{"MCP":"python", "content":"len((c for c in 'strawberry' if c='r'))"}
The attention mechanism allow LLMs to learn this kind of absurdly inefficient calculations. But we really shouldn't use LLMs where they're outperformed by trivial existing solutions.
At the end of the day we can get so much done just by breaking down a problem into smaller problems.
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So substitute another phrase, if you prefer. It doesn't change the logic. "Specifically, we define a formal world where bungling is defined as inconsistencies between a computable LLM and a computable ground truth function. By employing results from learning theory, we show that LLMs cannot learn all the computable functions and will therefore inevitably bungle if used as general problem solvers."
Their diagonalization argument applies to any system that uses finite training data. Calling such a system "LLM" is an (unintentional) red herring.