51–60 of 140 posts
I'm glad that we're making progress towards a deeper understanding of what LLMs are inherently good at and what they're inherently bad at (not to say incapable of doing, but stuff that is less likely to work due to fundamental limitations). There's similarity here with, for example, defining the architecture of software, but letting an LLM write the functions. Or asking an LLM to write you the SQL query for your data…
> due to fundamental limitations People keep throwing this phrase around in relation to LLMs, when not a single “fundamental limitation” has been rigorously demonstrated to exist, and many tasks that were claimed to be impossible for LLMs two years ago supposedly due to “fundamental limitations” (e.g. character counting or phonetics) are non-issues for them today even without tools.
Some limitations are not rigorously demonstrated to be fundamental, but continuously present from the first early LLMs yes. Shouldn't the burden of proof be on those who say it can be done?
And some limitations are fundamental, and have been rigorously demonstrated, e.g.:
Earlier quoted context omitted.
of course, if you choose to ignore all the limitations they indeed have no limitations.
Nobody says they have no limitations. The question is are those limitation fundamental, i.e. can we expect improvement, say within a year.
There are also limitations due to maths and/or physics that aren't fixable under any design. Outside science fiction, there is no technology whose limitations are all fixable.
Here's one: https://arxiv.org/abs/2401.11817?utm_source=chatgpt.com
Earlier quoted context omitted.
[flagged]
Every decent human artist knows to draw a sketch before painting something.
But it's not universally true, particularly among artists working in the last 100 years or so. Certainly Jackson Pollock (whether one regards his work as good or not) didn't sketch out how he was going to distribute paint onto canvas. Another example is Morris Luis (and other "stain painters") who didn't sketch out how he applied paint to canvas.
You're comment is largely correct, just pointing out that more than a few "decent artists" didn't (or don't) work that way.
Has anyone built a platform which has image to image pipelines and lets you use prompt to SVG generation from SOTA LLMs?
Earlier quoted context omitted.
Even the original stable diffusion app had image 2 image. It just didn’t work as well. I‘m not sure why this is supposed to be novel.
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.
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 multimodal LLMs with "take detailed content, make targeted edits to it". So I'm glad the OP posted it.
I'm glad that we're making progress towards a deeper understanding of what LLMs are inherently good at and what they're inherently bad at (not to say incapable of doing, but stuff that is less likely to work due to fundamental limitations). There's similarity here with, for example, defining the architecture of software, but letting an LLM write the functions. Or asking an LLM to write you the SQL query for your data…
> due to fundamental limitations People keep throwing this phrase around in relation to LLMs, when not a single “fundamental limitation” has been rigorously demonstrated to exist, and many tasks that were claimed to be impossible for LLMs two years ago supposedly due to “fundamental limitations” (e.g. character counting or phonetics) are non-issues for them today even without tools.
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…