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Like if I type "I have two appl.." for example, often it will suggest "apple" singular instead of plural. Just a small example, but it is really bad at predicting which variant of a word should come after the previous

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[-] 0x4E4F@sh.itjust.works 118 points 11 months ago

Phones don't use LLM for predictive text. The algos are a lot less complex on phones.

[-] Knusper@feddit.de 18 points 11 months ago

I guess, the real question is: Could we be using (simplistic) LLMs on a phone for predictive text?

There's some LLMs that can be run offline and which maybe wouldn't use enormous amounts of battery. But I don't know how good the quality of those is...

[-] ashe@lemmy.starless.one 45 points 11 months ago* (last edited 11 months ago)

You can run an LLM on a phone (tried it myself once, with llama.cpp), but even on the simplest model I could find it was doing maybe one word every few seconds while using up 100% of the CPU. The quality is terrible, and your battery wouldn't last an hour.

[-] astraeus@programming.dev 3 points 11 months ago

Does the AI processing have to be performed locally or constantly active?

[-] EatYouWell@lemmy.world 15 points 11 months ago

No, but you open up a can of worms from a security aspect if you send it out to be processed.

[-] Scubus@sh.itjust.works 7 points 11 months ago

I'm sure every phone having a keylogger won't end badly

[-] Zippy@lemmy.world 3 points 11 months ago

And latency.

[-] bassomitron@lemmy.world 15 points 11 months ago* (last edited 11 months ago)

The kind of local/offline LLMs that would work on your phone would not be very good quality. There's been amazing progress in quantization of LLMs to get them working on weaker GPUs with lower VRAM and CPUs, so maybe it'll occur, but I'm not an expert.

I also don't foresee them linking it up to a cloud-based LLM as that'd be a shit load of queries and extremely expensive.

[-] astraeus@programming.dev 1 points 11 months ago* (last edited 11 months ago)

OpenAI is probably already handling a significant amount of queries, I think for daily use the LLM should simply initialize a word map based on user history and then update it semi-occasionally, like once a week or two. Most people don’t drastically change their vocabulary in the course of a few weeks

[-] EatYouWell@lemmy.world 1 points 11 months ago

We're talking about orders of magnitude more queries if we start offloading predective text like that.

[-] SpooksMcDoots@mander.xyz 6 points 11 months ago

Openhermes 2.5 Mistral 7b competes with LLMs that require 10x the resources. You could try it out on your phone.

[-] Mr_Blott@lemmy.world 6 points 11 months ago

That was my next question, thanks!

Didn't think of battery use, makes sense

[-] Munkisquisher@lemmy.nz 5 points 11 months ago

A pre trained model isn't going to learn how you type the more you use it. Though with Microsoft owning SwiftKey, I imagine they will try it soon

[-] SidewaysHighways@lemmy.world 5 points 11 months ago

I was so heartbroken when I found out that Microsoft purchased Swiftkey. It was my favorite. Is there any way to still use it without Microsoft involved? Lawdhammercy

[-] neptune@dmv.social 4 points 11 months ago

I think apple has pitched this for a future iPhone, yes.

[-] squaresinger@feddit.de 3 points 11 months ago

They'll probably have to offload that to a server farm in real time. That's not gonna be easy.

[-] 0x4E4F@sh.itjust.works 0 points 11 months ago

I guess... why not... but the db is probably huge, like in the hundreds of GB (maybe even TB... who knows), can't run that offline.

[-] Arthur@literature.cafe 12 points 11 months ago

iOS 17 uses a small gpt-2 based model for predictive text.

[-] 0x4E4F@sh.itjust.works 5 points 11 months ago

Hm, that's interesting 👍.

[-] Kichae@lemmy.ca 0 points 11 months ago

The algorithms are the same. The models are different, being trained on a smaller data set.

[-] FooBarrington@lemmy.world 5 points 11 months ago* (last edited 11 months ago)

No, the algorithms are not the same. Phones don't use transformer models for text prediction, they use Markov chain-based approaches. Also, retraining of transformer models for individualized completion would be too expensive, whereas it's basically free with Markov approaches. Where do you get these ideas?

[-] 0x4E4F@sh.itjust.works 2 points 11 months ago* (last edited 11 months ago)

Perhaps, I'm not a dev, especially not an iOS or an Android one.

[-] Endorkend@kbin.social 86 points 11 months ago* (last edited 11 months ago)

This is an engine.

And this is an engine.

[-] FilthyHookerSpit@lemmy.world 12 points 11 months ago
[-] Lmaydev@programming.dev 38 points 11 months ago* (last edited 11 months ago)

AI is a vast field. LLMs and neural networks are a small part of it.

LLMs are very expensive to run and a lot more complex than the markov chains often used for predictive text.

Predictive text just chooses a likely word based on what's typed. This may be as simple as looking for words that start with what you've typed.

LLMs vectorise words and understand the complex relationship between vectors using many data points. So it would spot the word "two" and realise that plurals are used with it.

[-] Dr_Cog@mander.xyz 4 points 11 months ago

Predictive text also can vectorize words, but the number of vectors per word are much, much simpler.

[-] squaresinger@feddit.de 34 points 11 months ago

Now guess how it feels to type German with predictive text. Most of our words can have more than a dozen different word endings depending on time and how the word is used. And that's not taking into account that we use compound words, which word prediction pretty much cannot predict and often doesn't even know. So spell check will mark a legal compound word as misspelled, because it doesn't understand the concept of compound words and doesn't know this specific word combination.

To show what I mean, the term "Danube steam boat captain's hat" becomes "DonauDampfSchiffKapitänsMütze" (I added capital letters which shouldn't be there to show where the next word in the compound word begins).

While this is an extreme example, it's pretty common for compound words to consist of 4-5 words.

[-] agressivelyPassive@feddit.de 8 points 11 months ago

And for some reason, some cases seem to be missing completely on my Android default keyboard. "untersuchst", just like a bunch of second person cases for slightly unusual words is non existent.

[-] squaresinger@feddit.de 4 points 11 months ago

Yeah, noticed that too. This is really annoying.

[-] Bigmouse@lemmy.world 1 points 11 months ago

My favourite: 'geröntgt' which is the second participle of 'röntgen' to x-ray someone. Never heard it pronounced correctly by a native speaker.

[-] max@feddit.nl 6 points 11 months ago

Dutch also has the issue with the compound words. Autocorrect will often put a space in there, which is grammatically incorrect (and ugly). I feel like it’s at a point now where the incorrect space usage has become mainstream and might change the language rules. Oh well.

[-] sir_reginald@lemmy.world 24 points 11 months ago* (last edited 11 months ago)

LLMs are orders of magnitude more sophisticated and expensive to run. But don't worry, I'm sure not so far in the future we will see smaller LLMs being run on device to be used as autocorrect.

[-] pacoboyd@lemm.ee 5 points 11 months ago

It would have to be pretty specific and small to work on a phone and I think a side effect would be everyone's conversations start to sound a lot more homogeneous.

[-] sir_reginald@lemmy.world 3 points 11 months ago

you're not wrong. Google just announced Gemini Nano that will run directly on the Pixel 8. Of course, it's the first of it's kind and will probably be slow and it's not used as autocorrect yet. But just give it one year or two and it will probably be more common.

[-] neptune@dmv.social 2 points 11 months ago

Even give years ago, Google had a keyboard that skimmed your emails and texts to start a bank of words you use to supplement it's dictionary for autocorrect. Like if you are a chemist and send an email that includes the word "tetrahydrafuran" every couple month, it would be nice for your phone keyboard to just have it in the dictionary.

[-] pacoboyd@lemm.ee 1 points 11 months ago

SwiftKey does that if you give it access to your emails.

[-] Mr_Blott@lemmy.world 3 points 11 months ago

Can we have Scottish ones that know what a bawbag is, and when to put an "e" on the end of "shit"?

Thanks!

[-] OpenStars@kbin.social 1 points 11 months ago

Think of it from the LLM's perspective - in the general pool you have common English, you have less common variations such as this, and then you have whatever the heck people like Kid Rock are doing...

Bawitdaba, da bang, da dang diggy diggy
Diggy, said the boogie, said up jump the boogie

[-] CyanFen@lemmy.one 22 points 11 months ago

Because they're using different tech. That's like asking why do phone calls sound bad compared to voip calls. They're just using different tech.

[-] Candelestine@lemmy.world 18 points 11 months ago* (last edited 11 months ago)

Lawnmowers can't keep up with Ferraris either, despite both being vehicles.

edit for wording

[-] Knusper@feddit.de 8 points 11 months ago

You're in the No Stupid Questions community. Think about rule 7 in particular.

[-] Candelestine@lemmy.world 6 points 11 months ago

Ah, thank you. I'll edit.

[-] alvaro@social.graves.cl 22 points 11 months ago

@Mr_Blott@lemmy.world autocorrect doesn’t use LLMs, at least not yet generally. I suspect it is some kind of markov model or maybe?

[-] asterfield@lemmy.world 21 points 11 months ago

LLMs like chatgpt take a wild amount of resources to run.

If you want something as smart as gpt3 and you want it to run at typing speeds, you’ll need a gaming PC running it.

People just recently managed to run gpt3 strength models at all on ordinary laptop hardware (slowly).

There is currently no way to run something gpt4 strength on ordinary consumer hardware (I’m just guessing but I think it takes a few hundred gb of VRAM to run)

[-] doublejay1999@lemmy.world 6 points 11 months ago

What the duck are you talking about?

[-] Hanabie@sh.itjust.works 4 points 11 months ago

You're comparing apples to oranges.

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this post was submitted on 10 Dec 2023
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