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Lavalamp too hot (discuss.tchncs.de)
submitted 3 weeks ago* (last edited 3 weeks ago) by swiftywizard@discuss.tchncs.de to c/programmer_humor@programming.dev
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[-] ideonek@piefed.social 35 points 3 weeks ago
[-] FishFace@piefed.social 109 points 3 weeks ago

LLMs work by picking the next word* as the most likely candidate word given its training and the context. Sometimes it gets into a situation where the model's view of "context" doesn't change when the word is picked, so the next word is just the same. Then the same thing happens again and around we go. There are fail-safe mechanisms to try and prevent it but they don't work perfectly.

*Token

[-] ideonek@piefed.social 20 points 3 weeks ago

That was the answer I was looking for. So it's simmolar to "seahorse" emoji case, but this time.at some point he just glitched that most likely next world for this sentence is "or" and after adding the "or" is also "or" and after adding the next one is also "or", and after a 11th one... you may just as we'll commit. Since thats the same context as with 10.

Thanks!

[-] MonkderVierte@lemmy.zip 5 points 3 weeks ago

I've got it once in a "while it is not" "while it is" loop.

[-] bunchberry@lemmy.world 1 points 3 weeks ago

This happened to me a lot when I tried to run big models with low context windows. It would effectively run out of memory so each new token wouldn't actually be added to the context so it would just get stuck in an infinite loop repeating the previous token. It is possible that there was a memory issue on Google's end.

[-] FishFace@piefed.social 1 points 3 weeks ago

There is something wrong if it's not discarding old context to make room for new

[-] bunchberry@lemmy.world 1 points 3 weeks ago

At least llama.cpp doesn't seem to do that by default. If it overruns the context window it just blorps.

[-] FishFace@piefed.social 1 points 3 weeks ago

I think there are parameters for that, from googling.

[-] ch00f@lemmy.world 56 points 3 weeks ago
[-] kamenlady@lemmy.world 12 points 3 weeks ago
[-] Arghblarg@lemmy.ca 31 points 3 weeks ago

LLM showed its true nature, probabilistic bullshit generator that got caught in a strange attractor of some sort within its own matrix of lies.

[-] ech@lemmy.ca 28 points 3 weeks ago

It's like the text predictor on your phone. If you just keep hitting the next suggested word, you'll usually end up in a loop at some point. Same thing here, though admittedly much more advanced.

[-] vaultdweller013@sh.itjust.works 3 points 3 weeks ago

Example of my phone doing this.

I just want you are the only reason that you can't just forget that I don't have a way that I have a lot to the word you are not even going on the phone and you can call it the other way to the other one I know you are going out to talk about the time you are not even in a good place for the rest they'll have a little bit more mechanically and the rest is.

You can see it looping pretty damned quick with me just hitting the first suggestion after the initial I.

[-] MrScottyTay@sh.itjust.works 3 points 3 weeks ago

I think I will be in the office tomorrow so I can do it now and then I can do it now and then I can do it for you and your dad and dad and dad and dad and dad and dad and dad and dad and dad and dad

That was mine haha

[-] palordrolap@fedia.io 18 points 3 weeks ago

Unmentioned by other comments: The LLM is trying to follow the rule of three because sentences with an "A, B and/or C" structure tend to sound more punchy, knowledgeable and authoritative.

Yes, I did do that on purpose.

[-] Cevilia@lemmy.blahaj.zone 12 points 3 weeks ago

Not only that, but also "not only, but also" constructions, which sound more emphatic, conclusive, and relatable.

[-] luciferofastora@feddit.org 2 points 3 weeks ago

I used to think learning stylistic devices like this was just an idle fancy, a tool simply designed to analyse poems, one of the many things you're most certain you'll never need but have to learn in school.

What a fool I've been.

[-] kogasa@programming.dev 17 points 3 weeks ago

Turned into a sea lion

this post was submitted on 25 Jan 2026
484 points (97.5% liked)

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