I keep seeing lots of people saying "LLMs are like compilers/assemblers for prompts"

[millihertz@oldbytes.space]

@cwebber well, it was until C99 anyway...

[fogti@chaos.social]

@natty @cwebber Java2K (wait, that's more like stochastic interpreter)

[ansuz@gts.cryptography.dog]

@thomasjwebb @cwebber @joeyh ​

Local models like llama could be reworked to accept a seed for their RNG. There'd be less risk of them becoming unavailable, and they'd be both deterministic and reproducible, but they'd still be terrible for all the other reasons that LLMs are terrible .

"Sovereign" and reproducible slop is still just slop

[deech@mastodon.social]

@cwebber I think we can compromise and call them really shitty compilers.

[alienghic@timeloop.cafe]

@cwebber

I was thinking LLMs are like ouiji boards or tarot readings.

Semi random noise where meaning is imposed by the participating humans.

[sherwoodinc@floss.social]

@cwebber gamified transpilers at best

[foolishowl@social.coop]

@mcc @mntmn @cwebber Do you remember AskJeeves? A friend of mine worked for them, and told me that their whole thing had been natural language Web searches, but after a few years, their internal research showed that almost all their users were doing searches for literal text, or literal text connected with Boolean operators, just the way they used the other search engines. It wasn't that "natural language search" didn't work, it's that no one wanted to use it.

When I was looking up how to disable Google Assistant on my phone, a few of the articles I read opened with some claim that it was the primary reason to use an Android phone to begin with. But outside TV shows, I've rarely heard anyone trying to use it.

Corporations were trying to market GUI desktops for the Commodore 64.

I'm suspicious that there's really that much demand for natural language interfaces and skeumorphism. We've been using tools for two million years that usually don't much resemble the human body.

[grumble209@kolektiva.social]

@cwebber "LLMs are compilers for prompts" says a lot more about someone's ignorance about compilers than about their knowledge of LLMs.

It's so stupid, it's almost wearing coconut shells on your ears and yelling into a stick and hoping that pallets of food start falling from the sky.

[thomasjwebb@mastodon.social]

@ansuz @cwebber @joeyh I do want to play around with llama but that goes so against my instincts of always trying to make development put less strain on my computer (like I really hated how it feels vscode really bloated up). And while yeah, having the model and source code is certainly an improvement, my experience with getting AI/GPU stuff from the past up and running again is... not fun. Having to resurrect a 10 year old version of a model would definitely suck.

[murodegrizeco@toad.social]

@cwebber

LLM maybe may be "dissembler".

[m_22@universeodon.com]

@cwebber the methods used to prepare the data are similar (preprocessing, encoding, tokenization). If you turned the temperature on an LLM to 0 then it can be used to deterministically output the word with the highest probability at every step. People aren’t talking about that in this case, though.

[m_22@universeodon.com]

@cwebber even if it was set to be deterministic, it still wouldn’t reliably produce correct output.

[bit101@mstdn.social]

@cwebber I've seen this basic message about non-determinism at least 3 times in the past week. I'm glad to see it more. It's an important point.

[patrick_h_lauke@mastodon.social]

@cwebber hah, had similar reaction to exactly that misguided point a few weeks ago https://mastodon.social/@patrick_h_lauke/116023804209257572